diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 00acacc9..54b636e5 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -52,7 +52,7 @@ jobs:
 
       # TODO: don't continue on error
       - name: basedmypy stubtest
-        run: poetry run stubtest --concise --mypy-config-file=pyproject.toml --allowlist=tests/stubtest/allowlist.txt scipy
+        run: poetry run stubtest --concise --ignore-positional-only --mypy-config-file=pyproject.toml --allowlist=tests/stubtest/allowlist.txt scipy
         continue-on-error: true
 
       # TODO
diff --git a/README.md b/README.md
index f6f42f68..4b5720f4 100644
--- a/README.md
+++ b/README.md
@@ -104,7 +104,6 @@ pyright), the "type completeness score" is **42.4%**.
 | `scipy.odr`                       | 1: skeleton     |
 | `scipy.optimize`                  | 2: partial      |
 | `scipy.optimize.cython_optimize`  | 0: missing      |
-| `scipy.optimize.zeros`            | 0: missing      |
 | `scipy.signal`                    | 2: partial      |
 | `scipy.signal.windows`            | 1: skeleton     |
 | `scipy.sparse`                    | 2: partial      |
diff --git a/scipy-stubs/constants/codata.pyi b/scipy-stubs/constants/codata.pyi
deleted file mode 100644
index 3267c078..00000000
--- a/scipy-stubs/constants/codata.pyi
+++ /dev/null
@@ -1,2 +0,0 @@
-# This file is not meant for public use and will be removed in SciPy v2.0.0.
-__all__ = ()
diff --git a/scipy-stubs/constants/constants.pyi b/scipy-stubs/constants/constants.pyi
deleted file mode 100644
index 3267c078..00000000
--- a/scipy-stubs/constants/constants.pyi
+++ /dev/null
@@ -1,2 +0,0 @@
-# This file is not meant for public use and will be removed in SciPy v2.0.0.
-__all__ = ()
diff --git a/scipy-stubs/optimize/elementwise.pyi b/scipy-stubs/optimize/elementwise.pyi
deleted file mode 100644
index 4c2390a8..00000000
--- a/scipy-stubs/optimize/elementwise.pyi
+++ /dev/null
@@ -1,6 +0,0 @@
-from ._elementwise import (
-    bracket_minimum as bracket_minimum,
-    bracket_root as bracket_root,
-    find_minimum as find_minimum,
-    find_root as find_root,
-)
diff --git a/scipy-stubs/spatial/transform/rotation.pyi b/scipy-stubs/spatial/transform/rotation.pyi
index 3d355e14..ac7e1f0e 100644
--- a/scipy-stubs/spatial/transform/rotation.pyi
+++ b/scipy-stubs/spatial/transform/rotation.pyi
@@ -1,3 +1,3 @@
 from ._rotation import Rotation, Slerp
 
-__all__ = "Rotation", "Slerp"
+__all__ = ["Rotation", "Slerp"]
diff --git a/stubtest.txt b/stubtest.txt
deleted file mode 100644
index 681f5a85..00000000
--- a/stubtest.txt
+++ /dev/null
@@ -1,2954 +0,0 @@
-[1mscipy.LowLevelCallable.__getitem__(B[m variable differs from runtime type def (self: Untyped, idx: Untyped) -> Untyped
-[1mscipy._lib._boost_utils(B[m failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy._lib._boost_utils'
-[1mscipy._lib._ccallback.CFFIFuncP(B[m is not present at runtime
-[1mscipy._lib._ccallback.CFFIVoidP(B[m is not present at runtime
-[1mscipy._lib._ccallback.LowLevelCallable.__getitem__(B[m variable differs from runtime type def (self: Untyped, idx: Untyped) -> Untyped
-[1mscipy._lib._ccallback.PyCFuncPtr(B[m cannot be subclassed at runtime, but isn't marked with @final in the stub
-[1mscipy._lib._ccallback.__all__(B[m is not present at runtime
-[1mscipy._lib._highs_utils(B[m failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy._lib._highs_utils'
-[1mscipy._lib._pep440.Infinity(B[m is not a type
-[1mscipy._lib._pep440.NegativeInfinity(B[m is not a type
-[1mscipy._lib._unuran_utils(B[m failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy._lib._unuran_utils'
-[1mscipy._lib._util.Generator.__init__(B[m is inconsistent, stub does not have *args argument "args"
-[1mscipy._lib._util.Generator.beta(B[m is not present in stub
-[1mscipy._lib._util.Generator.binomial(B[m is not present in stub
-[1mscipy._lib._util.Generator.bit_generator(B[m is not present in stub
-[1mscipy._lib._util.Generator.bytes(B[m is not present in stub
-[1mscipy._lib._util.Generator.chisquare(B[m is not present in stub
-[1mscipy._lib._util.Generator.choice(B[m is not present in stub
-[1mscipy._lib._util.Generator.dirichlet(B[m is not present in stub
-[1mscipy._lib._util.Generator.exponential(B[m is not present in stub
-[1mscipy._lib._util.Generator.f(B[m is not present in stub
-[1mscipy._lib._util.Generator.gamma(B[m is not present in stub
-[1mscipy._lib._util.Generator.geometric(B[m is not present in stub
-[1mscipy._lib._util.Generator.gumbel(B[m is not present in stub
-[1mscipy._lib._util.Generator.hypergeometric(B[m is not present in stub
-[1mscipy._lib._util.Generator.integers(B[m is not present in stub
-[1mscipy._lib._util.Generator.laplace(B[m is not present in stub
-[1mscipy._lib._util.Generator.logistic(B[m is not present in stub
-[1mscipy._lib._util.Generator.lognormal(B[m is not present in stub
-[1mscipy._lib._util.Generator.logseries(B[m is not present in stub
-[1mscipy._lib._util.Generator.multinomial(B[m is not present in stub
-[1mscipy._lib._util.Generator.multivariate_hypergeometric(B[m is not present in stub
-[1mscipy._lib._util.Generator.multivariate_normal(B[m is not present in stub
-[1mscipy._lib._util.Generator.negative_binomial(B[m is not present in stub
-[1mscipy._lib._util.Generator.noncentral_chisquare(B[m is not present in stub
-[1mscipy._lib._util.Generator.noncentral_f(B[m is not present in stub
-[1mscipy._lib._util.Generator.normal(B[m is not present in stub
-[1mscipy._lib._util.Generator.pareto(B[m is not present in stub
-[1mscipy._lib._util.Generator.permutation(B[m is not present in stub
-[1mscipy._lib._util.Generator.permuted(B[m is not present in stub
-[1mscipy._lib._util.Generator.poisson(B[m is not present in stub
-[1mscipy._lib._util.Generator.power(B[m is not present in stub
-[1mscipy._lib._util.Generator.random(B[m is not present in stub
-[1mscipy._lib._util.Generator.rayleigh(B[m is not present in stub
-[1mscipy._lib._util.Generator.shuffle(B[m is not present in stub
-[1mscipy._lib._util.Generator.spawn(B[m is not present in stub
-[1mscipy._lib._util.Generator.standard_cauchy(B[m is not present in stub
-[1mscipy._lib._util.Generator.standard_exponential(B[m is not present in stub
-[1mscipy._lib._util.Generator.standard_gamma(B[m is not present in stub
-[1mscipy._lib._util.Generator.standard_normal(B[m is not present in stub
-[1mscipy._lib._util.Generator.standard_t(B[m is not present in stub
-[1mscipy._lib._util.Generator.triangular(B[m is not present in stub
-[1mscipy._lib._util.Generator.uniform(B[m is not present in stub
-[1mscipy._lib._util.Generator.vonmises(B[m is not present in stub
-[1mscipy._lib._util.Generator.wald(B[m is not present in stub
-[1mscipy._lib._util.Generator.weibull(B[m is not present in stub
-[1mscipy._lib._util.Generator.zipf(B[m is not present in stub
-[1mscipy._lib._util.SeedType(B[m is not present at runtime
-[1mscipy._lib.array_api_compat.__all__(B[m is not present at runtime
-[1mscipy._lib.array_api_compat.common.__all__(B[m is not present at runtime
-[1mscipy._lib.decorator.ContextManager.__init__(B[m is inconsistent, stub argument "func" differs from runtime argument "g"
-[1mscipy.constants.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.constants.codata(B[m is not present in stub
-[1mscipy.constants.constants(B[m is not present in stub
-[1mscipy.constants.codata.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.constants.codata.ConstantWarning(B[m is not present in stub
-[1mscipy.constants.codata.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.constants.codata.c(B[m is not present in stub
-[1mscipy.constants.codata.find(B[m is not present in stub
-[1mscipy.constants.codata.k(B[m is not present in stub
-[1mscipy.constants.codata.physical_constants(B[m is not present in stub
-[1mscipy.constants.codata.precision(B[m is not present in stub
-[1mscipy.constants.codata.unit(B[m is not present in stub
-[1mscipy.constants.codata.value(B[m is not present in stub
-[1mscipy.constants.constants.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.constants.constants.Avogadro(B[m is not present in stub
-[1mscipy.constants.constants.Boltzmann(B[m is not present in stub
-[1mscipy.constants.constants.Btu(B[m is not present in stub
-[1mscipy.constants.constants.Btu_IT(B[m is not present in stub
-[1mscipy.constants.constants.Btu_th(B[m is not present in stub
-[1mscipy.constants.constants.G(B[m is not present in stub
-[1mscipy.constants.constants.Julian_year(B[m is not present in stub
-[1mscipy.constants.constants.N_A(B[m is not present in stub
-[1mscipy.constants.constants.Planck(B[m is not present in stub
-[1mscipy.constants.constants.R(B[m is not present in stub
-[1mscipy.constants.constants.Rydberg(B[m is not present in stub
-[1mscipy.constants.constants.Stefan_Boltzmann(B[m is not present in stub
-[1mscipy.constants.constants.Wien(B[m is not present in stub
-[1mscipy.constants.constants.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.constants.constants.acre(B[m is not present in stub
-[1mscipy.constants.constants.alpha(B[m is not present in stub
-[1mscipy.constants.constants.angstrom(B[m is not present in stub
-[1mscipy.constants.constants.arcmin(B[m is not present in stub
-[1mscipy.constants.constants.arcminute(B[m is not present in stub
-[1mscipy.constants.constants.arcsec(B[m is not present in stub
-[1mscipy.constants.constants.arcsecond(B[m is not present in stub
-[1mscipy.constants.constants.astronomical_unit(B[m is not present in stub
-[1mscipy.constants.constants.atm(B[m is not present in stub
-[1mscipy.constants.constants.atmosphere(B[m is not present in stub
-[1mscipy.constants.constants.atomic_mass(B[m is not present in stub
-[1mscipy.constants.constants.atto(B[m is not present in stub
-[1mscipy.constants.constants.au(B[m is not present in stub
-[1mscipy.constants.constants.bar(B[m is not present in stub
-[1mscipy.constants.constants.barrel(B[m is not present in stub
-[1mscipy.constants.constants.bbl(B[m is not present in stub
-[1mscipy.constants.constants.blob(B[m is not present in stub
-[1mscipy.constants.constants.c(B[m is not present in stub
-[1mscipy.constants.constants.calorie(B[m is not present in stub
-[1mscipy.constants.constants.calorie_IT(B[m is not present in stub
-[1mscipy.constants.constants.calorie_th(B[m is not present in stub
-[1mscipy.constants.constants.carat(B[m is not present in stub
-[1mscipy.constants.constants.centi(B[m is not present in stub
-[1mscipy.constants.constants.convert_temperature(B[m is not present in stub
-[1mscipy.constants.constants.day(B[m is not present in stub
-[1mscipy.constants.constants.deci(B[m is not present in stub
-[1mscipy.constants.constants.degree(B[m is not present in stub
-[1mscipy.constants.constants.degree_Fahrenheit(B[m is not present in stub
-[1mscipy.constants.constants.deka(B[m is not present in stub
-[1mscipy.constants.constants.dyn(B[m is not present in stub
-[1mscipy.constants.constants.dyne(B[m is not present in stub
-[1mscipy.constants.constants.e(B[m is not present in stub
-[1mscipy.constants.constants.eV(B[m is not present in stub
-[1mscipy.constants.constants.electron_mass(B[m is not present in stub
-[1mscipy.constants.constants.electron_volt(B[m is not present in stub
-[1mscipy.constants.constants.elementary_charge(B[m is not present in stub
-[1mscipy.constants.constants.epsilon_0(B[m is not present in stub
-[1mscipy.constants.constants.erg(B[m is not present in stub
-[1mscipy.constants.constants.exa(B[m is not present in stub
-[1mscipy.constants.constants.exbi(B[m is not present in stub
-[1mscipy.constants.constants.femto(B[m is not present in stub
-[1mscipy.constants.constants.fermi(B[m is not present in stub
-[1mscipy.constants.constants.fine_structure(B[m is not present in stub
-[1mscipy.constants.constants.fluid_ounce(B[m is not present in stub
-[1mscipy.constants.constants.fluid_ounce_US(B[m is not present in stub
-[1mscipy.constants.constants.fluid_ounce_imp(B[m is not present in stub
-[1mscipy.constants.constants.foot(B[m is not present in stub
-[1mscipy.constants.constants.g(B[m is not present in stub
-[1mscipy.constants.constants.gallon(B[m is not present in stub
-[1mscipy.constants.constants.gallon_US(B[m is not present in stub
-[1mscipy.constants.constants.gallon_imp(B[m is not present in stub
-[1mscipy.constants.constants.gas_constant(B[m is not present in stub
-[1mscipy.constants.constants.gibi(B[m is not present in stub
-[1mscipy.constants.constants.giga(B[m is not present in stub
-[1mscipy.constants.constants.golden(B[m is not present in stub
-[1mscipy.constants.constants.golden_ratio(B[m is not present in stub
-[1mscipy.constants.constants.grain(B[m is not present in stub
-[1mscipy.constants.constants.gram(B[m is not present in stub
-[1mscipy.constants.constants.gravitational_constant(B[m is not present in stub
-[1mscipy.constants.constants.h(B[m is not present in stub
-[1mscipy.constants.constants.hbar(B[m is not present in stub
-[1mscipy.constants.constants.hectare(B[m is not present in stub
-[1mscipy.constants.constants.hecto(B[m is not present in stub
-[1mscipy.constants.constants.horsepower(B[m is not present in stub
-[1mscipy.constants.constants.hour(B[m is not present in stub
-[1mscipy.constants.constants.hp(B[m is not present in stub
-[1mscipy.constants.constants.inch(B[m is not present in stub
-[1mscipy.constants.constants.k(B[m is not present in stub
-[1mscipy.constants.constants.kgf(B[m is not present in stub
-[1mscipy.constants.constants.kibi(B[m is not present in stub
-[1mscipy.constants.constants.kilo(B[m is not present in stub
-[1mscipy.constants.constants.kilogram_force(B[m is not present in stub
-[1mscipy.constants.constants.kmh(B[m is not present in stub
-[1mscipy.constants.constants.knot(B[m is not present in stub
-[1mscipy.constants.constants.lambda2nu(B[m is not present in stub
-[1mscipy.constants.constants.lb(B[m is not present in stub
-[1mscipy.constants.constants.lbf(B[m is not present in stub
-[1mscipy.constants.constants.light_year(B[m is not present in stub
-[1mscipy.constants.constants.liter(B[m is not present in stub
-[1mscipy.constants.constants.litre(B[m is not present in stub
-[1mscipy.constants.constants.long_ton(B[m is not present in stub
-[1mscipy.constants.constants.m_e(B[m is not present in stub
-[1mscipy.constants.constants.m_n(B[m is not present in stub
-[1mscipy.constants.constants.m_p(B[m is not present in stub
-[1mscipy.constants.constants.m_u(B[m is not present in stub
-[1mscipy.constants.constants.mach(B[m is not present in stub
-[1mscipy.constants.constants.mebi(B[m is not present in stub
-[1mscipy.constants.constants.mega(B[m is not present in stub
-[1mscipy.constants.constants.metric_ton(B[m is not present in stub
-[1mscipy.constants.constants.micro(B[m is not present in stub
-[1mscipy.constants.constants.micron(B[m is not present in stub
-[1mscipy.constants.constants.mil(B[m is not present in stub
-[1mscipy.constants.constants.mile(B[m is not present in stub
-[1mscipy.constants.constants.milli(B[m is not present in stub
-[1mscipy.constants.constants.minute(B[m is not present in stub
-[1mscipy.constants.constants.mmHg(B[m is not present in stub
-[1mscipy.constants.constants.mph(B[m is not present in stub
-[1mscipy.constants.constants.mu_0(B[m is not present in stub
-[1mscipy.constants.constants.nano(B[m is not present in stub
-[1mscipy.constants.constants.nautical_mile(B[m is not present in stub
-[1mscipy.constants.constants.neutron_mass(B[m is not present in stub
-[1mscipy.constants.constants.nu2lambda(B[m is not present in stub
-[1mscipy.constants.constants.ounce(B[m is not present in stub
-[1mscipy.constants.constants.oz(B[m is not present in stub
-[1mscipy.constants.constants.parsec(B[m is not present in stub
-[1mscipy.constants.constants.pebi(B[m is not present in stub
-[1mscipy.constants.constants.peta(B[m is not present in stub
-[1mscipy.constants.constants.pi(B[m is not present in stub
-[1mscipy.constants.constants.pico(B[m is not present in stub
-[1mscipy.constants.constants.point(B[m is not present in stub
-[1mscipy.constants.constants.pound(B[m is not present in stub
-[1mscipy.constants.constants.pound_force(B[m is not present in stub
-[1mscipy.constants.constants.proton_mass(B[m is not present in stub
-[1mscipy.constants.constants.psi(B[m is not present in stub
-[1mscipy.constants.constants.pt(B[m is not present in stub
-[1mscipy.constants.constants.short_ton(B[m is not present in stub
-[1mscipy.constants.constants.sigma(B[m is not present in stub
-[1mscipy.constants.constants.slinch(B[m is not present in stub
-[1mscipy.constants.constants.slug(B[m is not present in stub
-[1mscipy.constants.constants.speed_of_light(B[m is not present in stub
-[1mscipy.constants.constants.speed_of_sound(B[m is not present in stub
-[1mscipy.constants.constants.stone(B[m is not present in stub
-[1mscipy.constants.constants.survey_foot(B[m is not present in stub
-[1mscipy.constants.constants.survey_mile(B[m is not present in stub
-[1mscipy.constants.constants.tebi(B[m is not present in stub
-[1mscipy.constants.constants.tera(B[m is not present in stub
-[1mscipy.constants.constants.ton_TNT(B[m is not present in stub
-[1mscipy.constants.constants.torr(B[m is not present in stub
-[1mscipy.constants.constants.troy_ounce(B[m is not present in stub
-[1mscipy.constants.constants.troy_pound(B[m is not present in stub
-[1mscipy.constants.constants.u(B[m is not present in stub
-[1mscipy.constants.constants.week(B[m is not present in stub
-[1mscipy.constants.constants.yard(B[m is not present in stub
-[1mscipy.constants.constants.year(B[m is not present in stub
-[1mscipy.constants.constants.yobi(B[m is not present in stub
-[1mscipy.constants.constants.yocto(B[m is not present in stub
-[1mscipy.constants.constants.yotta(B[m is not present in stub
-[1mscipy.constants.constants.zebi(B[m is not present in stub
-[1mscipy.constants.constants.zepto(B[m is not present in stub
-[1mscipy.constants.constants.zero_Celsius(B[m is not present in stub
-[1mscipy.constants.constants.zetta(B[m is not present in stub
-[1mscipy.fftpack.basic.fft(B[m is not present in stub
-[1mscipy.fftpack.basic.fft2(B[m is not present in stub
-[1mscipy.fftpack.basic.fftn(B[m is not present in stub
-[1mscipy.fftpack.basic.ifft(B[m is not present in stub
-[1mscipy.fftpack.basic.ifft2(B[m is not present in stub
-[1mscipy.fftpack.basic.ifftn(B[m is not present in stub
-[1mscipy.fftpack.basic.irfft(B[m is not present in stub
-[1mscipy.fftpack.basic.rfft(B[m is not present in stub
-[1mscipy.fftpack.helper.fftfreq(B[m is not present in stub
-[1mscipy.fftpack.helper.fftshift(B[m is not present in stub
-[1mscipy.fftpack.helper.ifftshift(B[m is not present in stub
-[1mscipy.fftpack.helper.next_fast_len(B[m is not present in stub
-[1mscipy.fftpack.helper.rfftfreq(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.cc_diff(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.convolve(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.cs_diff(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.diff(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.hilbert(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.ihilbert(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.itilbert(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.sc_diff(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.shift(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.ss_diff(B[m is not present in stub
-[1mscipy.fftpack.pseudo_diffs.tilbert(B[m is not present in stub
-[1mscipy.fftpack.realtransforms.dct(B[m is not present in stub
-[1mscipy.fftpack.realtransforms.dctn(B[m is not present in stub
-[1mscipy.fftpack.realtransforms.dst(B[m is not present in stub
-[1mscipy.fftpack.realtransforms.dstn(B[m is not present in stub
-[1mscipy.fftpack.realtransforms.idct(B[m is not present in stub
-[1mscipy.fftpack.realtransforms.idctn(B[m is not present in stub
-[1mscipy.fftpack.realtransforms.idst(B[m is not present in stub
-[1mscipy.fftpack.realtransforms.idstn(B[m is not present in stub
-[1mscipy.integrate.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.integrate.dblquad(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],)
-[1mscipy.integrate.dblquad(B[m is inconsistent, runtime argument "epsabs" is not keyword-only
-[1mscipy.integrate.dblquad(B[m is inconsistent, runtime argument "epsrel" is not keyword-only
-[1mscipy.integrate.fixed_quad(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],)
-[1mscipy.integrate.nquad(B[m is inconsistent, runtime argument "full_output" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "col_deriv" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "full_output" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "ml" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "mu" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "rtol" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "atol" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "tcrit" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "h0" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "hmax" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "hmin" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "ixpr" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "mxstep" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "mxhnil" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "mxordn" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "mxords" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "printmessg" is not keyword-only
-[1mscipy.integrate.odeint(B[m is inconsistent, runtime argument "tfirst" is not keyword-only
-[1mscipy.integrate.quad(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.integrate.quad(B[m is inconsistent, runtime argument "full_output" is not keyword-only
-[1mscipy.integrate.quad(B[m is inconsistent, stub does not have argument "epsabs"
-[1mscipy.integrate.quad(B[m is inconsistent, stub does not have argument "epsrel"
-[1mscipy.integrate.quad(B[m is inconsistent, stub does not have argument "limit"
-[1mscipy.integrate.quad(B[m is inconsistent, stub does not have argument "points"
-[1mscipy.integrate.quad(B[m is inconsistent, stub does not have argument "weight"
-[1mscipy.integrate.quad(B[m is inconsistent, stub does not have argument "wvar"
-[1mscipy.integrate.quad(B[m is inconsistent, stub does not have argument "wopts"
-[1mscipy.integrate.quad(B[m is inconsistent, runtime argument "maxp1" is not keyword-only
-[1mscipy.integrate.quad(B[m is inconsistent, runtime argument "limlst" is not keyword-only
-[1mscipy.integrate.quad(B[m is inconsistent, runtime argument "complex_func" is not keyword-only
-[1mscipy.integrate.quad(B[m is inconsistent, runtime does not have **kwargs argument "kwds"
-[1mscipy.integrate.quad_vec(B[m is inconsistent, runtime argument "cache_size" has a default value of type float, which is incompatible with stub argument type int. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.integrate.quad_vec(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-3 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.integrate.tplquad(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],)
-[1mscipy.integrate.tplquad(B[m is inconsistent, runtime argument "epsabs" is not keyword-only
-[1mscipy.integrate.tplquad(B[m is inconsistent, runtime argument "epsrel" is not keyword-only
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-[1mscipy.integrate._bvp.BVPResult.yp(B[m is not present at runtime
-[1mscipy.integrate._bvp.EPS(B[m is not present in stub
-[1mscipy.integrate._bvp.TERMINATION_MESSAGES(B[m is not present in stub
-[1mscipy.integrate._bvp.collocation_fun(B[m is not present in stub
-[1mscipy.integrate._bvp.compute_jac_indices(B[m is not present in stub
-[1mscipy.integrate._bvp.construct_global_jac(B[m is not present in stub
-[1mscipy.integrate._bvp.create_spline(B[m is not present in stub
-[1mscipy.integrate._bvp.estimate_bc_jac(B[m is not present in stub
-[1mscipy.integrate._bvp.estimate_fun_jac(B[m is not present in stub
-[1mscipy.integrate._bvp.estimate_rms_residuals(B[m is not present in stub
-[1mscipy.integrate._bvp.modify_mesh(B[m is not present in stub
-[1mscipy.integrate._bvp.prepare_sys(B[m is not present in stub
-[1mscipy.integrate._bvp.print_iteration_header(B[m is not present in stub
-[1mscipy.integrate._bvp.print_iteration_progress(B[m is not present in stub
-[1mscipy.integrate._bvp.solve_newton(B[m is not present in stub
-[1mscipy.integrate._bvp.stacked_matmul(B[m is not present in stub
-[1mscipy.integrate._bvp.wrap_functions(B[m is not present in stub
-[1mscipy.integrate._ivp.__all__(B[m is not present at runtime
-[1mscipy.integrate._ivp.ivp.prepare_events(B[m is inconsistent, runtime does not have argument "t0"
-[1mscipy.integrate._ode.IntegratorBase.scalar(B[m variable differs from runtime type type
-[1mscipy.integrate._ode.dop853.success(B[m variable differs from runtime type None
-[1mscipy.integrate._ode.dopri5.success(B[m variable differs from runtime type None
-[1mscipy.integrate._ode.lsoda.success(B[m variable differs from runtime type None
-[1mscipy.integrate._ode.vode.success(B[m variable differs from runtime type None
-[1mscipy.integrate._ode.zvode.scalar(B[m variable differs from runtime type type
-[1mscipy.integrate._ode.zvode.success(B[m variable differs from runtime type None
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-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "col_deriv" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "full_output" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "ml" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "mu" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "rtol" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "atol" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "tcrit" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "h0" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "hmax" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "hmin" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "ixpr" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "mxstep" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "mxhnil" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "mxordn" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "mxords" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "printmessg" is not keyword-only
-[1mscipy.integrate._odepack_py.odeint(B[m is inconsistent, runtime argument "tfirst" is not keyword-only
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-[1mscipy.integrate._quad_vec.quad_vec(B[m is inconsistent, runtime argument "cache_size" has a default value of type float, which is incompatible with stub argument type int. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.integrate._quad_vec.quad_vec(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-3 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.integrate._quadpack_py._NQuad.full_output(B[m is not present at runtime
-[1mscipy.integrate._quadpack_py._NQuad.func(B[m is not present at runtime
-[1mscipy.integrate._quadpack_py._NQuad.opts(B[m is not present at runtime
-[1mscipy.integrate._quadpack_py._NQuad.ranges(B[m is not present at runtime
-[1mscipy.integrate._quadpack_py._OptFunc.opt(B[m is not present at runtime
-[1mscipy.integrate._quadpack_py._RangeFunc.range_(B[m is not present at runtime
-[1mscipy.integrate._quadpack_py.dblquad(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],)
-[1mscipy.integrate._quadpack_py.dblquad(B[m is inconsistent, runtime argument "epsabs" is not keyword-only
-[1mscipy.integrate._quadpack_py.dblquad(B[m is inconsistent, runtime argument "epsrel" is not keyword-only
-[1mscipy.integrate._quadpack_py.nquad(B[m is inconsistent, runtime argument "full_output" is not keyword-only
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, runtime argument "full_output" is not keyword-only
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, stub does not have argument "epsabs"
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, stub does not have argument "epsrel"
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, stub does not have argument "limit"
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, stub does not have argument "points"
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, stub does not have argument "weight"
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, stub does not have argument "wvar"
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, stub does not have argument "wopts"
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, runtime argument "maxp1" is not keyword-only
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, runtime argument "limlst" is not keyword-only
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, runtime argument "complex_func" is not keyword-only
-[1mscipy.integrate._quadpack_py.quad(B[m is inconsistent, runtime does not have **kwargs argument "kwds"
-[1mscipy.integrate._quadpack_py.tplquad(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],)
-[1mscipy.integrate._quadpack_py.tplquad(B[m is inconsistent, runtime argument "epsabs" is not keyword-only
-[1mscipy.integrate._quadpack_py.tplquad(B[m is inconsistent, runtime argument "epsrel" is not keyword-only
-[1mscipy.integrate._quadrature.fixed_quad(B[m is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],)
-[1mscipy.integrate._tanhsinh.nsum(B[m is not present at runtime
-[1mscipy.integrate.lsoda.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.integrate.lsoda.lsoda(B[m is not present in stub
-[1mscipy.integrate.odepack.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.integrate.odepack.ODEintWarning(B[m is not present in stub
-[1mscipy.integrate.odepack.odeint(B[m is not present in stub
-[1mscipy.integrate.quadpack.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.integrate.quadpack.IntegrationWarning(B[m is not present in stub
-[1mscipy.integrate.quadpack.dblquad(B[m is not present in stub
-[1mscipy.integrate.quadpack.nquad(B[m is not present in stub
-[1mscipy.integrate.quadpack.quad(B[m is not present in stub
-[1mscipy.integrate.quadpack.tplquad(B[m is not present in stub
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-[1mscipy.interpolate.InterpolatedUnivariateSpline.__init__(B[m is inconsistent, runtime argument "bbox" is not keyword-only
-[1mscipy.interpolate.InterpolatedUnivariateSpline.__init__(B[m is inconsistent, runtime argument "k" is not keyword-only
-[1mscipy.interpolate.InterpolatedUnivariateSpline.__init__(B[m is inconsistent, runtime argument "ext" is not keyword-only
-[1mscipy.interpolate.InterpolatedUnivariateSpline.__init__(B[m is inconsistent, runtime argument "check_finite" is not keyword-only
-[1mscipy.interpolate.InterpolatedUnivariateSpline.__init__(B[m is inconsistent, runtime does not have argument "s"
-[1mscipy.interpolate.LSQBivariateSpline.__init__(B[m is inconsistent, runtime argument "w" is not keyword-only
-[1mscipy.interpolate.LSQBivariateSpline.__init__(B[m is inconsistent, runtime argument "bbox" is not keyword-only
-[1mscipy.interpolate.LSQBivariateSpline.__init__(B[m is inconsistent, runtime argument "kx" is not keyword-only
-[1mscipy.interpolate.LSQBivariateSpline.__init__(B[m is inconsistent, runtime argument "ky" is not keyword-only
-[1mscipy.interpolate.LSQBivariateSpline.__init__(B[m is inconsistent, runtime argument "eps" is not keyword-only
-[1mscipy.interpolate.LSQSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "w" is not keyword-only
-[1mscipy.interpolate.LSQSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "eps" is not keyword-only
-[1mscipy.interpolate.LSQUnivariateSpline.__init__(B[m is inconsistent, runtime argument "w" is not keyword-only
-[1mscipy.interpolate.LSQUnivariateSpline.__init__(B[m is inconsistent, runtime argument "bbox" is not keyword-only
-[1mscipy.interpolate.LSQUnivariateSpline.__init__(B[m is inconsistent, runtime argument "k" is not keyword-only
-[1mscipy.interpolate.LSQUnivariateSpline.__init__(B[m is inconsistent, runtime argument "ext" is not keyword-only
-[1mscipy.interpolate.LSQUnivariateSpline.__init__(B[m is inconsistent, runtime argument "check_finite" is not keyword-only
-[1mscipy.interpolate.LinAlgError(B[m is not present at runtime
-[1mscipy.interpolate.RectBivariateSpline.__init__(B[m is inconsistent, runtime argument "bbox" is not keyword-only
-[1mscipy.interpolate.RectBivariateSpline.__init__(B[m is inconsistent, runtime argument "kx" is not keyword-only
-[1mscipy.interpolate.RectBivariateSpline.__init__(B[m is inconsistent, runtime argument "ky" is not keyword-only
-[1mscipy.interpolate.RectBivariateSpline.__init__(B[m is inconsistent, runtime argument "s" is not keyword-only
-[1mscipy.interpolate.RectSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "pole_continuity" is not keyword-only
-[1mscipy.interpolate.RectSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "pole_values" is not keyword-only
-[1mscipy.interpolate.RectSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "pole_exact" is not keyword-only
-[1mscipy.interpolate.RectSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "pole_flat" is not keyword-only
-[1mscipy.interpolate.SmoothBivariateSpline.__init__(B[m is inconsistent, runtime argument "w" is not keyword-only
-[1mscipy.interpolate.SmoothBivariateSpline.__init__(B[m is inconsistent, runtime argument "bbox" is not keyword-only
-[1mscipy.interpolate.SmoothBivariateSpline.__init__(B[m is inconsistent, runtime argument "kx" is not keyword-only
-[1mscipy.interpolate.SmoothBivariateSpline.__init__(B[m is inconsistent, runtime argument "ky" is not keyword-only
-[1mscipy.interpolate.SmoothBivariateSpline.__init__(B[m is inconsistent, runtime argument "s" is not keyword-only
-[1mscipy.interpolate.SmoothBivariateSpline.__init__(B[m is inconsistent, runtime argument "eps" is not keyword-only
-[1mscipy.interpolate.SmoothSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "w" is not keyword-only
-[1mscipy.interpolate.SmoothSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "s" is not keyword-only
-[1mscipy.interpolate.SmoothSphereBivariateSpline.__init__(B[m is inconsistent, runtime argument "eps" is not keyword-only
-[1mscipy.interpolate.SphereBivariateSpline(B[m is not present at runtime
-[1mscipy.interpolate.UnivariateSpline.__init__(B[m is inconsistent, runtime argument "w" is not keyword-only
-[1mscipy.interpolate.UnivariateSpline.__init__(B[m is inconsistent, runtime argument "bbox" is not keyword-only
-[1mscipy.interpolate.UnivariateSpline.__init__(B[m is inconsistent, runtime argument "k" is not keyword-only
-[1mscipy.interpolate.UnivariateSpline.__init__(B[m is inconsistent, runtime argument "s" is not keyword-only
-[1mscipy.interpolate.UnivariateSpline.__init__(B[m is inconsistent, runtime argument "ext" is not keyword-only
-[1mscipy.interpolate.UnivariateSpline.__init__(B[m is inconsistent, runtime argument "check_finite" is not keyword-only
-[1mscipy.interpolate.check_random_state(B[m is not present at runtime
-[1mscipy.interpolate.cho_solve_banded(B[m is not present at runtime
-[1mscipy.interpolate.cholesky_banded(B[m is not present at runtime
-[1mscipy.interpolate.comb(B[m is not present at runtime
-[1mscipy.interpolate.copy_if_needed(B[m is not present at runtime
-[1mscipy.interpolate.csr_array(B[m is not present at runtime
-[1mscipy.interpolate.dblint(B[m is not present at runtime
-[1mscipy.interpolate.dfitpack_int(B[m is not present at runtime
-[1mscipy.interpolate.err_mesg(B[m is not present at runtime
-[1mscipy.interpolate.factorial(B[m is not present at runtime
-[1mscipy.interpolate.float_factorial(B[m is not present at runtime
-[1mscipy.interpolate.fpcheck(B[m is not present at runtime
-[1mscipy.interpolate.get_lapack_funcs(B[m is not present at runtime
-[1mscipy.interpolate.interpnd(B[m is not present in stub
-[1mscipy.interpolate.minimize_scalar(B[m is not present at runtime
-[1mscipy.interpolate.normalize_axis_index(B[m is not present at runtime
-[1mscipy.interpolate.poch(B[m is not present at runtime
-[1mscipy.interpolate.prepare_input(B[m is not present at runtime
-[1mscipy.interpolate.solve(B[m is not present at runtime
-[1mscipy.interpolate.solve_banded(B[m is not present at runtime
-[1mscipy.interpolate._bsplines.solve(B[m is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-[1mscipy.interpolate._cubic.solve(B[m is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-[1mscipy.interpolate._fitpack2.InterpolatedUnivariateSpline.__init__(B[m is inconsistent, runtime argument "w" is not keyword-only
-[1mscipy.interpolate._fitpack2.InterpolatedUnivariateSpline.__init__(B[m is inconsistent, runtime argument "bbox" is not keyword-only
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-[1mscipy.linalg.expm_frechet(B[m is inconsistent, stub argument "A" differs from runtime argument "E"
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, stub argument "A" has a default value but runtime argument does not
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, stub argument "E" differs from runtime argument "method"
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, runtime argument "method" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, stub argument "E" differs from runtime argument "compute_expm"
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, stub argument "method" differs from runtime argument "check_finite"
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'SPS' | 'blockEnlarge' | None. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, runtime does not have argument "method"
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, runtime does not have argument "compute_expm"
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, runtime does not have argument "compute_expm"
-[1mscipy.linalg.expm_frechet(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg.invpascal(B[m is inconsistent, stub argument "n" has a default value but runtime argument does not
-[1mscipy.linalg.invpascal(B[m is inconsistent, stub argument "n" differs from runtime argument "kind"
-[1mscipy.linalg.invpascal(B[m is inconsistent, runtime argument "kind" has a default value of type 'symmetric', which is incompatible with stub argument type int | numpy.integer[numpy._typing.NBitBase] | numpy.bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.invpascal(B[m is inconsistent, stub argument "kind" differs from runtime argument "exact"
-[1mscipy.linalg.invpascal(B[m is inconsistent, runtime argument "exact" has a default value of type True, which is incompatible with stub argument type 'symmetric' | 'upper' | 'lower'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.invpascal(B[m is inconsistent, runtime does not have argument "kind"
-[1mscipy.linalg.invpascal(B[m is inconsistent, runtime does not have argument "exact"
-[1mscipy.linalg.invpascal(B[m is inconsistent, runtime does not have argument "exact"
-[1mscipy.linalg.lu(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg.lu(B[m is inconsistent, stub argument "a" differs from runtime argument "permute_l"
-[1mscipy.linalg.lu(B[m is inconsistent, stub argument "permute_l" differs from runtime argument "overwrite_a"
-[1mscipy.linalg.lu(B[m is inconsistent, stub argument "permute_l" differs from runtime argument "check_finite"
-[1mscipy.linalg.lu(B[m is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.lu(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "p_indices"
-[1mscipy.linalg.lu(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg.lu(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg.lu(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg.lu(B[m is inconsistent, runtime does not have argument "p_indices"
-[1mscipy.linalg.lu(B[m is inconsistent, runtime does not have argument "p_indices"
-[1mscipy.linalg.matfuncs(B[m is not present in stub
-[1mscipy.linalg.matmul_toeplitz(B[m is inconsistent, runtime argument "check_finite" has a default value of False, which is different from stub argument default True
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, stub argument "A" has a default value but runtime argument does not
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, stub argument "A" differs from runtime argument "permute"
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, stub argument "permute" differs from runtime argument "scale"
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, stub argument "permute" differs from runtime argument "separate"
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, stub argument "scale" differs from runtime argument "overwrite_a"
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, runtime does not have argument "scale"
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, runtime does not have argument "separate"
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, runtime does not have argument "separate"
-[1mscipy.linalg.matrix_balance(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg.misc(B[m is not present in stub
-[1mscipy.linalg.null_space(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg.null_space(B[m is inconsistent, runtime does not have argument "lapack_driver"
-[1mscipy.linalg.null_space(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg.pascal(B[m is inconsistent, stub argument "n" has a default value but runtime argument does not
-[1mscipy.linalg.pascal(B[m is inconsistent, stub argument "n" differs from runtime argument "kind"
-[1mscipy.linalg.pascal(B[m is inconsistent, runtime argument "kind" has a default value of type 'symmetric', which is incompatible with stub argument type int | numpy.integer[numpy._typing.NBitBase] | numpy.bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.pascal(B[m is inconsistent, stub argument "kind" differs from runtime argument "exact"
-[1mscipy.linalg.pascal(B[m is inconsistent, runtime argument "exact" has a default value of type True, which is incompatible with stub argument type 'symmetric' | 'upper' | 'lower'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.pascal(B[m is inconsistent, runtime does not have argument "kind"
-[1mscipy.linalg.pascal(B[m is inconsistent, runtime does not have argument "exact"
-[1mscipy.linalg.pascal(B[m is inconsistent, runtime does not have argument "exact"
-[1mscipy.linalg.pinvh(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg.pinvh(B[m is inconsistent, stub argument "a" differs from runtime argument "atol"
-[1mscipy.linalg.pinvh(B[m is inconsistent, runtime argument "atol" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.pinvh(B[m is inconsistent, stub argument "atol" differs from runtime argument "rtol"
-[1mscipy.linalg.pinvh(B[m is inconsistent, stub argument "atol" differs from runtime argument "lower"
-[1mscipy.linalg.pinvh(B[m is inconsistent, stub argument "rtol" differs from runtime argument "return_rank"
-[1mscipy.linalg.pinvh(B[m is inconsistent, stub argument "rtol" differs from runtime argument "check_finite"
-[1mscipy.linalg.pinvh(B[m is inconsistent, runtime does not have argument "lower"
-[1mscipy.linalg.pinvh(B[m is inconsistent, runtime does not have argument "lower"
-[1mscipy.linalg.pinvh(B[m is inconsistent, runtime does not have argument "return_rank"
-[1mscipy.linalg.pinvh(B[m is inconsistent, runtime does not have argument "return_rank"
-[1mscipy.linalg.pinvh(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg.qr(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg.qr(B[m is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-[1mscipy.linalg.qr(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-[1mscipy.linalg.qr(B[m is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.qr(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-[1mscipy.linalg.qr(B[m is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.qr(B[m is inconsistent, stub argument "lwork" differs from runtime argument "pivoting"
-[1mscipy.linalg.qr(B[m is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-[1mscipy.linalg.qr(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.linalg.qr(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.linalg.qr(B[m is inconsistent, runtime does not have argument "pivoting"
-[1mscipy.linalg.qr(B[m is inconsistent, runtime does not have argument "pivoting"
-[1mscipy.linalg.qr(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, stub argument "a" differs from runtime argument "c"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, stub argument "c" differs from runtime argument "mode"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, stub argument "c" differs from runtime argument "pivoting"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, stub argument "mode" differs from runtime argument "conjugate"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, runtime argument "conjugate" has a default value of type False, which is incompatible with stub argument type 'left' | 'right'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, stub argument "mode" differs from runtime argument "overwrite_a"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, runtime argument "overwrite_a" has a default value of type False, which is incompatible with stub argument type 'left' | 'right'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, stub argument "pivoting" differs from runtime argument "overwrite_c"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, runtime does not have argument "pivoting"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, runtime does not have argument "conjugate"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg.qr_multiply(B[m is inconsistent, runtime does not have argument "overwrite_c"
-[1mscipy.linalg.rq(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg.rq(B[m is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-[1mscipy.linalg.rq(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-[1mscipy.linalg.rq(B[m is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.rq(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-[1mscipy.linalg.rq(B[m is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.rq(B[m is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-[1mscipy.linalg.rq(B[m is inconsistent, runtime does not have argument "lwork"
-[1mscipy.linalg.rq(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.linalg.rq(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.linalg.rq(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg.solve(B[m is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-[1mscipy.linalg.special_matrices(B[m is not present in stub
-[1mscipy.linalg.svd(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg.svd(B[m is inconsistent, stub argument "a" differs from runtime argument "full_matrices"
-[1mscipy.linalg.svd(B[m is inconsistent, stub argument "full_matrices" differs from runtime argument "compute_uv"
-[1mscipy.linalg.svd(B[m is inconsistent, stub argument "full_matrices" differs from runtime argument "overwrite_a"
-[1mscipy.linalg.svd(B[m is inconsistent, stub argument "compute_uv" differs from runtime argument "check_finite"
-[1mscipy.linalg.svd(B[m is inconsistent, stub argument "compute_uv" differs from runtime argument "lapack_driver"
-[1mscipy.linalg.svd(B[m is inconsistent, runtime argument "lapack_driver" has a default value of type 'gesdd', which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg.svd(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg.svd(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg.svd(B[m is inconsistent, runtime does not have argument "lapack_driver"
-[1mscipy.linalg._basic.matmul_toeplitz(B[m is inconsistent, runtime argument "check_finite" has a default value of False, which is different from stub argument default True
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, stub argument "A" has a default value but runtime argument does not
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, stub argument "A" differs from runtime argument "permute"
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, stub argument "permute" differs from runtime argument "scale"
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, stub argument "permute" differs from runtime argument "separate"
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, stub argument "scale" differs from runtime argument "overwrite_a"
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, runtime does not have argument "scale"
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, runtime does not have argument "separate"
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, runtime does not have argument "separate"
-[1mscipy.linalg._basic.matrix_balance(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, stub argument "a" differs from runtime argument "atol"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, runtime argument "atol" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, stub argument "atol" differs from runtime argument "rtol"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, stub argument "atol" differs from runtime argument "lower"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, stub argument "rtol" differs from runtime argument "return_rank"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, stub argument "rtol" differs from runtime argument "check_finite"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, runtime does not have argument "lower"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, runtime does not have argument "lower"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, runtime does not have argument "return_rank"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, runtime does not have argument "return_rank"
-[1mscipy.linalg._basic.pinvh(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._basic.solve(B[m is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-[1mscipy.linalg._cythonized_array_utils.numeric_t(B[m is not present at runtime
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, stub argument "a" differs from runtime argument "b"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, runtime argument "b" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, stub argument "b" differs from runtime argument "left"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, stub argument "b" differs from runtime argument "right"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, stub argument "left" differs from runtime argument "overwrite_a"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, stub argument "left" differs from runtime argument "overwrite_b"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, stub argument "right" differs from runtime argument "check_finite"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, stub argument "right" differs from runtime argument "homogeneous_eigvals"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, runtime argument "homogeneous_eigvals" has a default value of type False, which is incompatible with stub argument type True. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, runtime does not have argument "overwrite_b"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._decomp.eig(B[m is inconsistent, runtime does not have argument "homogeneous_eigvals"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, stub argument "a_band" has a default value but runtime argument does not
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, stub argument "a_band" differs from runtime argument "lower"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, stub argument "lower" differs from runtime argument "eigvals_only"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, stub argument "lower" differs from runtime argument "overwrite_a_band"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, stub argument "eigvals_only" differs from runtime argument "select"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, runtime argument "select" has a default value of type 'a', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, stub argument "eigvals_only" differs from runtime argument "select_range"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, runtime argument "select_range" has a default value of type None, which is incompatible with stub argument type True. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, stub argument "overwrite_a_band" differs from runtime argument "max_ev"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, stub argument "select" differs from runtime argument "check_finite"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'a' | 'v' | 'i'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, runtime does not have argument "select_range"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, runtime does not have argument "max_ev"
-[1mscipy.linalg._decomp.eig_banded(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._decomp.eigvals_banded(B[m is inconsistent, runtime argument "lower" has a default value of False, which is different from stub argument default True
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, stub argument "a" differs from runtime argument "permute_l"
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, stub argument "permute_l" differs from runtime argument "overwrite_a"
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, stub argument "permute_l" differs from runtime argument "check_finite"
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "p_indices"
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, runtime does not have argument "p_indices"
-[1mscipy.linalg._decomp_lu.lu(B[m is inconsistent, runtime does not have argument "p_indices"
-[1mscipy.linalg._decomp_lu_cython.__all__(B[m is not present at runtime
-[1mscipy.linalg._decomp_lu_cython.lu_decompose(B[m is not present at runtime
-[1mscipy.linalg._decomp_lu_cython.lu_dispatcher(B[m is inconsistent, stub argument "lu" differs from runtime argument "u"
-[1mscipy.linalg._decomp_lu_cython.lu_dispatcher(B[m is inconsistent, stub argument "perm" differs from runtime argument "piv"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, stub argument "lwork" differs from runtime argument "pivoting"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, runtime does not have argument "pivoting"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, runtime does not have argument "pivoting"
-[1mscipy.linalg._decomp_qr.qr(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, stub argument "a" differs from runtime argument "c"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, stub argument "c" differs from runtime argument "mode"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, stub argument "c" differs from runtime argument "pivoting"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, stub argument "mode" differs from runtime argument "conjugate"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, runtime argument "conjugate" has a default value of type False, which is incompatible with stub argument type 'left' | 'right'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, stub argument "mode" differs from runtime argument "overwrite_a"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, runtime argument "overwrite_a" has a default value of type False, which is incompatible with stub argument type 'left' | 'right'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, stub argument "pivoting" differs from runtime argument "overwrite_c"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, runtime does not have argument "pivoting"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, runtime does not have argument "conjugate"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg._decomp_qr.qr_multiply(B[m is inconsistent, runtime does not have argument "overwrite_c"
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, runtime does not have argument "lwork"
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.linalg._decomp_qr.rq(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._decomp_svd.null_space(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._decomp_svd.null_space(B[m is inconsistent, runtime does not have argument "lapack_driver"
-[1mscipy.linalg._decomp_svd.null_space(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, stub argument "a" differs from runtime argument "full_matrices"
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, stub argument "full_matrices" differs from runtime argument "compute_uv"
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, stub argument "full_matrices" differs from runtime argument "overwrite_a"
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, stub argument "compute_uv" differs from runtime argument "check_finite"
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, stub argument "compute_uv" differs from runtime argument "lapack_driver"
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, runtime argument "lapack_driver" has a default value of type 'gesdd', which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._decomp_svd.svd(B[m is inconsistent, runtime does not have argument "lapack_driver"
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, stub argument "A" has a default value but runtime argument does not
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, stub argument "A" differs from runtime argument "E"
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, stub argument "A" has a default value but runtime argument does not
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, stub argument "E" differs from runtime argument "method"
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, runtime argument "method" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, stub argument "E" differs from runtime argument "compute_expm"
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, stub argument "method" differs from runtime argument "check_finite"
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'SPS' | 'blockEnlarge' | None. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, runtime does not have argument "method"
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, runtime does not have argument "compute_expm"
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, runtime does not have argument "compute_expm"
-[1mscipy.linalg._expm_frechet.expm_frechet(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, stub argument "A" has a default value but runtime argument does not
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, stub argument "A" differs from runtime argument "E"
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, stub argument "A" has a default value but runtime argument does not
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, stub argument "E" differs from runtime argument "method"
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, runtime argument "method" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, stub argument "E" differs from runtime argument "compute_expm"
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, stub argument "method" differs from runtime argument "check_finite"
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'SPS' | 'blockEnlarge' | None. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, runtime does not have argument "method"
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, runtime does not have argument "compute_expm"
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, runtime does not have argument "compute_expm"
-[1mscipy.linalg._matfuncs.expm_frechet(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.linalg._matfuncs_expm.pick_pade_structure(B[m is inconsistent, stub argument "a" differs from runtime argument "Am"
-[1mscipy.linalg._solve_toeplitz.__all__(B[m is not present at runtime
-[1mscipy.linalg._special_matrices.invpascal(B[m is inconsistent, stub argument "n" has a default value but runtime argument does not
-[1mscipy.linalg._special_matrices.invpascal(B[m is inconsistent, stub argument "n" differs from runtime argument "kind"
-[1mscipy.linalg._special_matrices.invpascal(B[m is inconsistent, runtime argument "kind" has a default value of type 'symmetric', which is incompatible with stub argument type int | numpy.integer[numpy._typing.NBitBase] | numpy.bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._special_matrices.invpascal(B[m is inconsistent, stub argument "kind" differs from runtime argument "exact"
-[1mscipy.linalg._special_matrices.invpascal(B[m is inconsistent, runtime argument "exact" has a default value of type True, which is incompatible with stub argument type 'symmetric' | 'upper' | 'lower'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._special_matrices.invpascal(B[m is inconsistent, runtime does not have argument "kind"
-[1mscipy.linalg._special_matrices.invpascal(B[m is inconsistent, runtime does not have argument "exact"
-[1mscipy.linalg._special_matrices.invpascal(B[m is inconsistent, runtime does not have argument "exact"
-[1mscipy.linalg._special_matrices.pascal(B[m is inconsistent, stub argument "n" has a default value but runtime argument does not
-[1mscipy.linalg._special_matrices.pascal(B[m is inconsistent, stub argument "n" differs from runtime argument "kind"
-[1mscipy.linalg._special_matrices.pascal(B[m is inconsistent, runtime argument "kind" has a default value of type 'symmetric', which is incompatible with stub argument type int | numpy.integer[numpy._typing.NBitBase] | numpy.bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._special_matrices.pascal(B[m is inconsistent, stub argument "kind" differs from runtime argument "exact"
-[1mscipy.linalg._special_matrices.pascal(B[m is inconsistent, runtime argument "exact" has a default value of type True, which is incompatible with stub argument type 'symmetric' | 'upper' | 'lower'. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.linalg._special_matrices.pascal(B[m is inconsistent, runtime does not have argument "kind"
-[1mscipy.linalg._special_matrices.pascal(B[m is inconsistent, runtime does not have argument "exact"
-[1mscipy.linalg._special_matrices.pascal(B[m is inconsistent, runtime does not have argument "exact"
-[1mscipy.linalg.basic.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.basic.LinAlgError(B[m is not present in stub
-[1mscipy.linalg.basic.LinAlgWarning(B[m is not present in stub
-[1mscipy.linalg.basic.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.basic.det(B[m is not present in stub
-[1mscipy.linalg.basic.get_lapack_funcs(B[m is not present in stub
-[1mscipy.linalg.basic.inv(B[m is not present in stub
-[1mscipy.linalg.basic.lstsq(B[m is not present in stub
-[1mscipy.linalg.basic.matmul_toeplitz(B[m is not present in stub
-[1mscipy.linalg.basic.matrix_balance(B[m is not present in stub
-[1mscipy.linalg.basic.pinv(B[m is not present in stub
-[1mscipy.linalg.basic.pinvh(B[m is not present in stub
-[1mscipy.linalg.basic.solve(B[m is not present in stub
-[1mscipy.linalg.basic.solve_banded(B[m is not present in stub
-[1mscipy.linalg.basic.solve_circulant(B[m is not present in stub
-[1mscipy.linalg.basic.solve_toeplitz(B[m is not present in stub
-[1mscipy.linalg.basic.solve_triangular(B[m is not present in stub
-[1mscipy.linalg.basic.solveh_banded(B[m is not present in stub
-[1mscipy.linalg.decomp.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.decomp.LinAlgError(B[m is not present in stub
-[1mscipy.linalg.decomp.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.decomp.cdf2rdf(B[m is not present in stub
-[1mscipy.linalg.decomp.eig(B[m is not present in stub
-[1mscipy.linalg.decomp.eig_banded(B[m is not present in stub
-[1mscipy.linalg.decomp.eigh(B[m is not present in stub
-[1mscipy.linalg.decomp.eigh_tridiagonal(B[m is not present in stub
-[1mscipy.linalg.decomp.eigvals(B[m is not present in stub
-[1mscipy.linalg.decomp.eigvals_banded(B[m is not present in stub
-[1mscipy.linalg.decomp.eigvalsh(B[m is not present in stub
-[1mscipy.linalg.decomp.eigvalsh_tridiagonal(B[m is not present in stub
-[1mscipy.linalg.decomp.get_lapack_funcs(B[m is not present in stub
-[1mscipy.linalg.decomp.hessenberg(B[m is not present in stub
-[1mscipy.linalg.decomp.norm(B[m is not present in stub
-[1mscipy.linalg.decomp_cholesky.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.decomp_cholesky.LinAlgError(B[m is not present in stub
-[1mscipy.linalg.decomp_cholesky.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.decomp_cholesky.cho_factor(B[m is not present in stub
-[1mscipy.linalg.decomp_cholesky.cho_solve(B[m is not present in stub
-[1mscipy.linalg.decomp_cholesky.cho_solve_banded(B[m is not present in stub
-[1mscipy.linalg.decomp_cholesky.cholesky(B[m is not present in stub
-[1mscipy.linalg.decomp_cholesky.cholesky_banded(B[m is not present in stub
-[1mscipy.linalg.decomp_cholesky.get_lapack_funcs(B[m is not present in stub
-[1mscipy.linalg.decomp_lu.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.decomp_lu.LinAlgWarning(B[m is not present in stub
-[1mscipy.linalg.decomp_lu.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.decomp_lu.get_lapack_funcs(B[m is not present in stub
-[1mscipy.linalg.decomp_lu.lu(B[m is not present in stub
-[1mscipy.linalg.decomp_lu.lu_factor(B[m is not present in stub
-[1mscipy.linalg.decomp_lu.lu_solve(B[m is not present in stub
-[1mscipy.linalg.decomp_qr.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.decomp_qr.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.decomp_qr.get_lapack_funcs(B[m is not present in stub
-[1mscipy.linalg.decomp_qr.qr(B[m is not present in stub
-[1mscipy.linalg.decomp_qr.qr_multiply(B[m is not present in stub
-[1mscipy.linalg.decomp_qr.rq(B[m is not present in stub
-[1mscipy.linalg.decomp_schur.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.decomp_schur.LinAlgError(B[m is not present in stub
-[1mscipy.linalg.decomp_schur.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.decomp_schur.eigvals(B[m is not present in stub
-[1mscipy.linalg.decomp_schur.get_lapack_funcs(B[m is not present in stub
-[1mscipy.linalg.decomp_schur.norm(B[m is not present in stub
-[1mscipy.linalg.decomp_schur.rsf2csf(B[m is not present in stub
-[1mscipy.linalg.decomp_schur.schur(B[m is not present in stub
-[1mscipy.linalg.decomp_svd.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.decomp_svd.LinAlgError(B[m is not present in stub
-[1mscipy.linalg.decomp_svd.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.decomp_svd.diagsvd(B[m is not present in stub
-[1mscipy.linalg.decomp_svd.get_lapack_funcs(B[m is not present in stub
-[1mscipy.linalg.decomp_svd.null_space(B[m is not present in stub
-[1mscipy.linalg.decomp_svd.orth(B[m is not present in stub
-[1mscipy.linalg.decomp_svd.subspace_angles(B[m is not present in stub
-[1mscipy.linalg.decomp_svd.svd(B[m is not present in stub
-[1mscipy.linalg.decomp_svd.svdvals(B[m is not present in stub
-[1mscipy.linalg.interpolative.estimate_rank(B[m is inconsistent, runtime does not have argument "rng"
-[1mscipy.linalg.interpolative.estimate_spectral_norm(B[m is inconsistent, runtime does not have argument "rng"
-[1mscipy.linalg.interpolative.estimate_spectral_norm_diff(B[m is inconsistent, runtime does not have argument "rng"
-[1mscipy.linalg.interpolative.interp_decomp(B[m is inconsistent, runtime does not have argument "rng"
-[1mscipy.linalg.interpolative.seed(B[m is inconsistent, runtime argument "seed" has a default value of type None, which is incompatible with stub argument type Never
-[1mscipy.linalg.interpolative.svd(B[m is inconsistent, runtime does not have argument "rng"
-[1mscipy.linalg.matfuncs.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.matfuncs.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.matfuncs.coshm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.cosm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.expm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.expm_cond(B[m is not present in stub
-[1mscipy.linalg.matfuncs.expm_frechet(B[m is not present in stub
-[1mscipy.linalg.matfuncs.fractional_matrix_power(B[m is not present in stub
-[1mscipy.linalg.matfuncs.funm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.inv(B[m is not present in stub
-[1mscipy.linalg.matfuncs.khatri_rao(B[m is not present in stub
-[1mscipy.linalg.matfuncs.logm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.norm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.rsf2csf(B[m is not present in stub
-[1mscipy.linalg.matfuncs.schur(B[m is not present in stub
-[1mscipy.linalg.matfuncs.signm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.sinhm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.sinm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.solve(B[m is not present in stub
-[1mscipy.linalg.matfuncs.sqrtm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.svd(B[m is not present in stub
-[1mscipy.linalg.matfuncs.tanhm(B[m is not present in stub
-[1mscipy.linalg.matfuncs.tanm(B[m is not present in stub
-[1mscipy.linalg.misc.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.linalg.misc.LinAlgError(B[m is not present in stub
-[1mscipy.linalg.misc.LinAlgWarning(B[m is not present in stub
-[1mscipy.linalg.misc.__all__(B[m variable differs from runtime type list[Untyped]
-[1mscipy.linalg.misc.get_blas_funcs(B[m is not present in stub
-[1mscipy.linalg.misc.get_lapack_funcs(B[m is not present in stub
-[1mscipy.linalg.misc.norm(B[m is not present in stub
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-[1mscipy.linalg.special_matrices.__all__(B[m variable differs from runtime type list[Untyped]
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-[1mscipy.linalg.special_matrices.dft(B[m is not present in stub
-[1mscipy.linalg.special_matrices.fiedler(B[m is not present in stub
-[1mscipy.linalg.special_matrices.fiedler_companion(B[m is not present in stub
-[1mscipy.linalg.special_matrices.hadamard(B[m is not present in stub
-[1mscipy.linalg.special_matrices.hankel(B[m is not present in stub
-[1mscipy.linalg.special_matrices.helmert(B[m is not present in stub
-[1mscipy.linalg.special_matrices.hilbert(B[m is not present in stub
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-[1mscipy.linalg.special_matrices.pascal(B[m is not present in stub
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-[1mscipy.misc.doccer.filldoc(B[m is not present in stub
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-[1mscipy.misc.doccer.inherit_docstring_from(B[m is not present in stub
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-[1mscipy.ndimage.filters.generic_gradient_magnitude(B[m is not present in stub
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-[1mscipy.ndimage.morphology.binary_propagation(B[m is not present in stub
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-[1mscipy.ndimage.morphology.grey_dilation(B[m is not present in stub
-[1mscipy.ndimage.morphology.grey_erosion(B[m is not present in stub
-[1mscipy.ndimage.morphology.grey_opening(B[m is not present in stub
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-[1mscipy.odr.odrpack.Model(B[m is not present in stub
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-[1mscipy.optimize._constraints.NonlinearConstraint.__init__(B[m is inconsistent, runtime argument "hess" has a default value of <scipy.optimize._hessian_update_strategy.BFGS object at 0x718076291ba0>, which is different from stub argument default None
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-[1mscipy.optimize._elementwise(B[m failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.optimize._elementwise'
-[1mscipy.optimize._group_columns.__pythran__(B[m is not present in stub
-[1mscipy.optimize._highs.cython(B[m failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.optimize._highs.cython'
-[1mscipy.optimize._highs.cython.src(B[m failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.optimize._highs.cython'
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-[1mscipy.optimize._linprog_util._LPProblem.__new__(B[m is inconsistent, runtime argument "b_eq" has a default value but stub argument does not
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-[1mscipy.optimize._linprog_util._LPProblem.__new__(B[m is inconsistent, runtime argument "x0" has a default value but stub argument does not
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-[1mscipy.optimize._lsq.least_squares(B[m is inconsistent, runtime argument "kwargs" has a default value of {}, which is different from stub argument default None
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-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
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-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
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-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, stub argument "lwork" differs from runtime argument "pivoting"
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-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, runtime does not have argument "mode"
-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, runtime does not have argument "pivoting"
-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, runtime does not have argument "pivoting"
-[1mscipy.optimize._lsq.trf.qr(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, stub argument "a" has a default value but runtime argument does not
-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, stub argument "a" differs from runtime argument "full_matrices"
-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, stub argument "full_matrices" differs from runtime argument "compute_uv"
-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, stub argument "full_matrices" differs from runtime argument "overwrite_a"
-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, stub argument "compute_uv" differs from runtime argument "check_finite"
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-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, runtime argument "lapack_driver" has a default value of type 'gesdd', which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, runtime does not have argument "overwrite_a"
-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, runtime does not have argument "check_finite"
-[1mscipy.optimize._lsq.trf.svd(B[m is inconsistent, runtime does not have argument "lapack_driver"
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-[1mscipy.optimize._nonlin.GenericBroyden.setup(B[m is inconsistent, stub argument "F" differs from runtime argument "f0"
-[1mscipy.optimize._nonlin.GenericBroyden.update(B[m is inconsistent, stub argument "F" differs from runtime argument "f"
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-[1mscipy.optimize._nonlin.KrylovJacobian.update(B[m is inconsistent, stub argument "F" differs from runtime argument "f"
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-[1mscipy.optimize._trustregion_constr.projections.LinearOperator.__new__(B[m is inconsistent, stub does not have *args argument "args"
-[1mscipy.optimize._trustregion_constr.projections.LinearOperator.dtype(B[m is not present at runtime
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-[1mscipy.optimize.minpack.fsolve(B[m is not present in stub
-[1mscipy.optimize.minpack.least_squares(B[m is not present in stub
-[1mscipy.optimize.minpack.leastsq(B[m is not present in stub
-[1mscipy.optimize.minpack.zeros(B[m is not present in stub
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-[1mscipy.optimize.nonlin.broyden1(B[m is not present in stub
-[1mscipy.optimize.nonlin.broyden2(B[m is not present in stub
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-[1mscipy.sparse._bsr._bsr_base.__init__(B[m is inconsistent, runtime does not have argument "maxprint"
-[1mscipy.sparse._bsr._bsr_base.tocoo(B[m is inconsistent, runtime argument "copy" has a default value of True, which is different from stub argument default False
-[1mscipy.sparse._compressed._cs_matrix.__init__(B[m is inconsistent, runtime does not have argument "maxprint"
-[1mscipy.sparse._compressed._cs_matrix.tocoo(B[m is inconsistent, runtime argument "copy" has a default value of True, which is different from stub argument default False
-[1mscipy.sparse._coo._coo_base.__init__(B[m is inconsistent, runtime does not have argument "maxprint"
-[1mscipy.sparse._csr._csr_base.tobsr(B[m is inconsistent, runtime argument "copy" has a default value of True, which is different from stub argument default False
-[1mscipy.sparse._data._data_matrix.__init__(B[m is inconsistent, runtime does not have argument "maxprint"
-[1mscipy.sparse._data._data_matrix.arcsin(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.arcsinh(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.arctan(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.arctanh(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.ceil(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.count_nonzero(B[m is inconsistent, runtime does not have argument "axis"
-[1mscipy.sparse._data._data_matrix.deg2rad(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.expm1(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.floor(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.log1p(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.rad2deg(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.rint(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.sign(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.sin(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.sinh(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.sqrt(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.tan(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.tanh(B[m is not present in stub
-[1mscipy.sparse._data._data_matrix.trunc(B[m is not present in stub
-[1mscipy.sparse._data._minmax_mixin.argmax(B[m is inconsistent, runtime does not have argument "explicit"
-[1mscipy.sparse._data._minmax_mixin.argmin(B[m is inconsistent, runtime does not have argument "explicit"
-[1mscipy.sparse._data._minmax_mixin.max(B[m is inconsistent, runtime does not have argument "explicit"
-[1mscipy.sparse._data._minmax_mixin.min(B[m is inconsistent, runtime does not have argument "explicit"
-[1mscipy.sparse._data._minmax_mixin.nanmax(B[m is inconsistent, runtime does not have argument "explicit"
-[1mscipy.sparse._data._minmax_mixin.nanmin(B[m is inconsistent, runtime does not have argument "explicit"
-[1mscipy.sparse._dia._dia_base.__init__(B[m is inconsistent, runtime does not have argument "maxprint"
-[1mscipy.sparse._dia._dia_base.count_nonzero(B[m is inconsistent, runtime does not have argument "axis"
-[1mscipy.sparse._dok._dok_base.__init__(B[m is inconsistent, runtime does not have argument "maxprint"
-[1mscipy.sparse._dok._dok_base.count_nonzero(B[m is inconsistent, runtime does not have argument "axis"
-[1mscipy.sparse._generate_sparsetools(B[m failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.sparse._generate_sparsetools'
-[1mscipy.sparse._index.INT_TYPES(B[m is not present in stub
-[1mscipy.sparse._lil._lil_base.__init__(B[m is inconsistent, runtime does not have argument "maxprint"
-[1mscipy.sparse._lil._lil_base.count_nonzero(B[m is inconsistent, runtime does not have argument "axis"
-[1mscipy.sparse._matrix.__all__(B[m is not present at runtime
-[1mscipy.sparse._matrix.spmatrix.todense(B[m is not present at runtime
-[1mscipy.sparse._sputils.convert_pydata_sparse_to_scipy(B[m is inconsistent, runtime does not have argument "accept_fv"
-[1mscipy.sparse.base.MAXPRINT(B[m is not present in stub
-[1mscipy.sparse.base.SparseEfficiencyWarning(B[m is not present in stub
-[1mscipy.sparse.base.SparseFormatWarning(B[m is not present in stub
-[1mscipy.sparse.base.SparseWarning(B[m is not present in stub
-[1mscipy.sparse.base.asmatrix(B[m is not present in stub
-[1mscipy.sparse.base.check_reshape_kwargs(B[m is not present in stub
-[1mscipy.sparse.base.check_shape(B[m is not present in stub
-[1mscipy.sparse.base.get_sum_dtype(B[m is not present in stub
-[1mscipy.sparse.base.isdense(B[m is not present in stub
-[1mscipy.sparse.base.isscalarlike(B[m is not present in stub
-[1mscipy.sparse.base.issparse(B[m is not present in stub
-[1mscipy.sparse.base.isspmatrix(B[m is not present in stub
-[1mscipy.sparse.base.spmatrix(B[m is not present in stub
-[1mscipy.sparse.base.validateaxis(B[m is not present in stub
-[1mscipy.sparse.bsr.bsr_matmat(B[m is not present in stub
-[1mscipy.sparse.bsr.bsr_matrix(B[m is not present in stub
-[1mscipy.sparse.bsr.bsr_matvec(B[m is not present in stub
-[1mscipy.sparse.bsr.bsr_matvecs(B[m is not present in stub
-[1mscipy.sparse.bsr.bsr_sort_indices(B[m is not present in stub
-[1mscipy.sparse.bsr.bsr_tocsr(B[m is not present in stub
-[1mscipy.sparse.bsr.bsr_transpose(B[m is not present in stub
-[1mscipy.sparse.bsr.check_shape(B[m is not present in stub
-[1mscipy.sparse.bsr.csr_matmat_maxnnz(B[m is not present in stub
-[1mscipy.sparse.bsr.getdata(B[m is not present in stub
-[1mscipy.sparse.bsr.getdtype(B[m is not present in stub
-[1mscipy.sparse.bsr.isshape(B[m is not present in stub
-[1mscipy.sparse.bsr.isspmatrix_bsr(B[m is not present in stub
-[1mscipy.sparse.bsr.spmatrix(B[m is not present in stub
-[1mscipy.sparse.bsr.to_native(B[m is not present in stub
-[1mscipy.sparse.bsr.upcast(B[m is not present in stub
-[1mscipy.sparse.bsr.warn(B[m is not present in stub
-[1mscipy.sparse.compressed.IndexMixin(B[m is not present in stub
-[1mscipy.sparse.compressed.SparseEfficiencyWarning(B[m is not present in stub
-[1mscipy.sparse.compressed.check_shape(B[m is not present in stub
-[1mscipy.sparse.compressed.csr_column_index1(B[m is not present in stub
-[1mscipy.sparse.compressed.csr_column_index2(B[m is not present in stub
-[1mscipy.sparse.compressed.csr_row_index(B[m is not present in stub
-[1mscipy.sparse.compressed.csr_row_slice(B[m is not present in stub
-[1mscipy.sparse.compressed.csr_sample_offsets(B[m is not present in stub
-[1mscipy.sparse.compressed.csr_sample_values(B[m is not present in stub
-[1mscipy.sparse.compressed.csr_todense(B[m is not present in stub
-[1mscipy.sparse.compressed.downcast_intp_index(B[m is not present in stub
-[1mscipy.sparse.compressed.get_csr_submatrix(B[m is not present in stub
-[1mscipy.sparse.compressed.get_sum_dtype(B[m is not present in stub
-[1mscipy.sparse.compressed.getdtype(B[m is not present in stub
-[1mscipy.sparse.compressed.is_pydata_spmatrix(B[m is not present in stub
-[1mscipy.sparse.compressed.isdense(B[m is not present in stub
-[1mscipy.sparse.compressed.isintlike(B[m is not present in stub
-[1mscipy.sparse.compressed.isscalarlike(B[m is not present in stub
-[1mscipy.sparse.compressed.isshape(B[m is not present in stub
-[1mscipy.sparse.compressed.operator(B[m is not present in stub
-[1mscipy.sparse.compressed.to_native(B[m is not present in stub
-[1mscipy.sparse.compressed.upcast(B[m is not present in stub
-[1mscipy.sparse.compressed.upcast_char(B[m is not present in stub
-[1mscipy.sparse.compressed.warn(B[m is not present in stub
-[1mscipy.sparse.construct.block_diag(B[m is not present in stub
-[1mscipy.sparse.construct.bmat(B[m is not present in stub
-[1mscipy.sparse.construct.bsr_matrix(B[m is not present in stub
-[1mscipy.sparse.construct.check_random_state(B[m is not present in stub
-[1mscipy.sparse.construct.coo_matrix(B[m is not present in stub
-[1mscipy.sparse.construct.csc_matrix(B[m is not present in stub
-[1mscipy.sparse.construct.csr_hstack(B[m is not present in stub
-[1mscipy.sparse.construct.csr_matrix(B[m is not present in stub
-[1mscipy.sparse.construct.dia_matrix(B[m is not present in stub
-[1mscipy.sparse.construct.diags(B[m is not present in stub
-[1mscipy.sparse.construct.eye(B[m is not present in stub
-[1mscipy.sparse.construct.get_index_dtype(B[m is not present in stub
-[1mscipy.sparse.construct.hstack(B[m is not present in stub
-[1mscipy.sparse.construct.identity(B[m is not present in stub
-[1mscipy.sparse.construct.isscalarlike(B[m is not present in stub
-[1mscipy.sparse.construct.issparse(B[m is not present in stub
-[1mscipy.sparse.construct.kron(B[m is not present in stub
-[1mscipy.sparse.construct.kronsum(B[m is not present in stub
-[1mscipy.sparse.construct.numbers(B[m is not present in stub
-[1mscipy.sparse.construct.rand(B[m is not present in stub
-[1mscipy.sparse.construct.random(B[m is not present in stub
-[1mscipy.sparse.construct.rng_integers(B[m is not present in stub
-[1mscipy.sparse.construct.spdiags(B[m is not present in stub
-[1mscipy.sparse.construct.upcast(B[m is not present in stub
-[1mscipy.sparse.construct.vstack(B[m is not present in stub
-[1mscipy.sparse.coo.SparseEfficiencyWarning(B[m is not present in stub
-[1mscipy.sparse.coo.check_reshape_kwargs(B[m is not present in stub
-[1mscipy.sparse.coo.check_shape(B[m is not present in stub
-[1mscipy.sparse.coo.coo_matrix(B[m is not present in stub
-[1mscipy.sparse.coo.coo_matvec(B[m is not present in stub
-[1mscipy.sparse.coo.coo_tocsr(B[m is not present in stub
-[1mscipy.sparse.coo.coo_todense(B[m is not present in stub
-[1mscipy.sparse.coo.downcast_intp_index(B[m is not present in stub
-[1mscipy.sparse.coo.getdata(B[m is not present in stub
-[1mscipy.sparse.coo.getdtype(B[m is not present in stub
-[1mscipy.sparse.coo.isshape(B[m is not present in stub
-[1mscipy.sparse.coo.isspmatrix_coo(B[m is not present in stub
-[1mscipy.sparse.coo.operator(B[m is not present in stub
-[1mscipy.sparse.coo.spmatrix(B[m is not present in stub
-[1mscipy.sparse.coo.to_native(B[m is not present in stub
-[1mscipy.sparse.coo.upcast(B[m is not present in stub
-[1mscipy.sparse.coo.upcast_char(B[m is not present in stub
-[1mscipy.sparse.coo.warn(B[m is not present in stub
-[1mscipy.sparse.csc.csc_matrix(B[m is not present in stub
-[1mscipy.sparse.csc.csc_tocsr(B[m is not present in stub
-[1mscipy.sparse.csc.expandptr(B[m is not present in stub
-[1mscipy.sparse.csc.isspmatrix_csc(B[m is not present in stub
-[1mscipy.sparse.csc.spmatrix(B[m is not present in stub
-[1mscipy.sparse.csc.upcast(B[m is not present in stub
-[1mscipy.sparse.csgraph.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.sparse.csgraph.NegativeCycleError.__init__(B[m is inconsistent, stub does not have argument "message"
-[1mscipy.sparse.csgraph.NegativeCycleError.__init__(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.bellman_ford(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.bellman_ford(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.bellman_ford(B[m is inconsistent, stub does not have argument "indices"
-[1mscipy.sparse.csgraph.bellman_ford(B[m is inconsistent, stub does not have argument "return_predecessors"
-[1mscipy.sparse.csgraph.bellman_ford(B[m is inconsistent, stub does not have argument "unweighted"
-[1mscipy.sparse.csgraph.bellman_ford(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.bellman_ford(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.breadth_first_order(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.breadth_first_order(B[m is inconsistent, stub does not have argument "i_start"
-[1mscipy.sparse.csgraph.breadth_first_order(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.breadth_first_order(B[m is inconsistent, stub does not have argument "return_predecessors"
-[1mscipy.sparse.csgraph.breadth_first_order(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.breadth_first_order(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.breadth_first_tree(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.breadth_first_tree(B[m is inconsistent, stub does not have argument "i_start"
-[1mscipy.sparse.csgraph.breadth_first_tree(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.breadth_first_tree(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.breadth_first_tree(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.connected_components(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.connected_components(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.connected_components(B[m is inconsistent, stub does not have argument "connection"
-[1mscipy.sparse.csgraph.connected_components(B[m is inconsistent, stub does not have argument "return_labels"
-[1mscipy.sparse.csgraph.connected_components(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.connected_components(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.construct_dist_matrix(B[m is inconsistent, stub does not have argument "graph"
-[1mscipy.sparse.csgraph.construct_dist_matrix(B[m is inconsistent, stub does not have argument "predecessors"
-[1mscipy.sparse.csgraph.construct_dist_matrix(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.construct_dist_matrix(B[m is inconsistent, stub does not have argument "null_value"
-[1mscipy.sparse.csgraph.construct_dist_matrix(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.construct_dist_matrix(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.csgraph_from_dense(B[m is inconsistent, stub does not have argument "graph"
-[1mscipy.sparse.csgraph.csgraph_from_dense(B[m is inconsistent, stub does not have argument "null_value"
-[1mscipy.sparse.csgraph.csgraph_from_dense(B[m is inconsistent, stub does not have argument "nan_null"
-[1mscipy.sparse.csgraph.csgraph_from_dense(B[m is inconsistent, stub does not have argument "infinity_null"
-[1mscipy.sparse.csgraph.csgraph_from_dense(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.csgraph_from_dense(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.csgraph_from_masked(B[m is inconsistent, stub does not have argument "graph"
-[1mscipy.sparse.csgraph.csgraph_from_masked(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.csgraph_from_masked(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.csgraph_masked_from_dense(B[m is inconsistent, stub does not have argument "graph"
-[1mscipy.sparse.csgraph.csgraph_masked_from_dense(B[m is inconsistent, stub does not have argument "null_value"
-[1mscipy.sparse.csgraph.csgraph_masked_from_dense(B[m is inconsistent, stub does not have argument "nan_null"
-[1mscipy.sparse.csgraph.csgraph_masked_from_dense(B[m is inconsistent, stub does not have argument "infinity_null"
-[1mscipy.sparse.csgraph.csgraph_masked_from_dense(B[m is inconsistent, stub does not have argument "copy"
-[1mscipy.sparse.csgraph.csgraph_masked_from_dense(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.csgraph_masked_from_dense(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.csgraph_to_dense(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.csgraph_to_dense(B[m is inconsistent, stub does not have argument "null_value"
-[1mscipy.sparse.csgraph.csgraph_to_dense(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.csgraph_to_dense(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.csgraph_to_masked(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.csgraph_to_masked(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.csgraph_to_masked(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.depth_first_order(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.depth_first_order(B[m is inconsistent, stub does not have argument "i_start"
-[1mscipy.sparse.csgraph.depth_first_order(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.depth_first_order(B[m is inconsistent, stub does not have argument "return_predecessors"
-[1mscipy.sparse.csgraph.depth_first_order(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.depth_first_order(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.depth_first_tree(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.depth_first_tree(B[m is inconsistent, stub does not have argument "i_start"
-[1mscipy.sparse.csgraph.depth_first_tree(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.depth_first_tree(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.depth_first_tree(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, stub does not have argument "indices"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, stub does not have argument "return_predecessors"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, stub does not have argument "unweighted"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, stub does not have argument "limit"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, stub does not have argument "min_only"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.dijkstra(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.floyd_warshall(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.floyd_warshall(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.floyd_warshall(B[m is inconsistent, stub does not have argument "return_predecessors"
-[1mscipy.sparse.csgraph.floyd_warshall(B[m is inconsistent, stub does not have argument "unweighted"
-[1mscipy.sparse.csgraph.floyd_warshall(B[m is inconsistent, stub does not have argument "overwrite"
-[1mscipy.sparse.csgraph.floyd_warshall(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.floyd_warshall(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.johnson(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.johnson(B[m is inconsistent, stub does not have argument "directed"
-[1mscipy.sparse.csgraph.johnson(B[m is inconsistent, stub does not have argument "indices"
-[1mscipy.sparse.csgraph.johnson(B[m is inconsistent, stub does not have argument "return_predecessors"
-[1mscipy.sparse.csgraph.johnson(B[m is inconsistent, stub does not have argument "unweighted"
-[1mscipy.sparse.csgraph.johnson(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.johnson(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.maximum_bipartite_matching(B[m is inconsistent, stub does not have argument "graph"
-[1mscipy.sparse.csgraph.maximum_bipartite_matching(B[m is inconsistent, stub does not have argument "perm_type"
-[1mscipy.sparse.csgraph.maximum_bipartite_matching(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.sparse.csgraph.maximum_bipartite_matching(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
-[1mscipy.sparse.csgraph.maximum_flow(B[m is inconsistent, stub does not have argument "csgraph"
-[1mscipy.sparse.csgraph.maximum_flow(B[m is inconsistent, stub does not have argument "source"
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-[1mscipy.sparse.csgraph.min_weight_full_bipartite_matching(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
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-[1mscipy.sparse.csgraph.minimum_spanning_tree(B[m is inconsistent, runtime does not have **kwargs argument "kwargs"
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-[1mscipy.spatial._kdtree.KDTree.__init__(B[m is inconsistent, stub does not have argument "balanced_tree"
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-[1mscipy.spatial.ckdtree.cKDTree.__init__(B[m is inconsistent, stub does not have *args argument "args"
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-[1mscipy.spatial.kdtree.KDTree.__init__(B[m is inconsistent, stub does not have argument "data"
-[1mscipy.spatial.kdtree.KDTree.__init__(B[m is inconsistent, stub does not have argument "leafsize"
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-[1mscipy.spatial.kdtree.KDTree.__init__(B[m is inconsistent, stub does not have argument "copy_data"
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-[1mscipy.special.btdtr(B[m variable differs from runtime type def (*args: Untyped, **kwargs: Untyped) -> Untyped
-[1mscipy.special.btdtri(B[m variable differs from runtime type def (*args: Untyped, **kwargs: Untyped) -> Untyped
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-[1mscipy.special.factorialk(B[m is inconsistent, runtime argument "exact" is not keyword-only
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-[1mscipy.special.perm(B[m is inconsistent, runtime argument "exact" is not keyword-only
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-[1mscipy.special._basic.factorial2(B[m is inconsistent, runtime argument "exact" is not keyword-only
-[1mscipy.special._basic.factorialk(B[m is inconsistent, runtime argument "exact" is not keyword-only
-[1mscipy.special._basic.perm(B[m is inconsistent, stub argument "n" differs from runtime argument "N"
-[1mscipy.special._basic.perm(B[m is inconsistent, runtime argument "exact" is not keyword-only
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-[1mscipy.special._orthogonal.he_roots(B[m is not present in stub
-[1mscipy.special._orthogonal.j_roots(B[m is not present in stub
-[1mscipy.special._orthogonal.js_roots(B[m is not present in stub
-[1mscipy.special._orthogonal.l_roots(B[m is not present in stub
-[1mscipy.special._orthogonal.la_roots(B[m is not present in stub
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-[1mscipy.special._orthogonal.ts_roots(B[m is not present in stub
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-[1mscipy.special._spherical_bessel.__all__(B[m is not present at runtime
-[1mscipy.special.cython_special.__all__(B[m is not present at runtime
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-[1mscipy.stats.BootstrapMethod.__init__(B[m is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
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-[1mscipy.stats.ECDFResult(B[m is not present at runtime
-[1mscipy.stats.EmpiricalDistributionFunction(B[m is not present at runtime
-[1mscipy.stats.FitError.__init__(B[m is inconsistent, stub does not have argument "msg"
-[1mscipy.stats.FitError.__init__(B[m is inconsistent, runtime does not have *args argument "args"
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-[1mscipy.stats.ansari(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.ansari(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats.argus(B[m is not present in stub
-[1mscipy.stats.bartlett(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.bartlett(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats.betabinom(B[m is not present in stub
-[1mscipy.stats.betanbinom(B[m is not present in stub
-[1mscipy.stats.betaprime(B[m is not present in stub
-[1mscipy.stats.biasedurn(B[m is not present in stub
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-[1mscipy.stats.boltzmann(B[m is not present in stub
-[1mscipy.stats.bradford(B[m is not present in stub
-[1mscipy.stats.brunnermunzel(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.brunnermunzel(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.burr(B[m is not present in stub
-[1mscipy.stats.burr12(B[m is not present in stub
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-[1mscipy.stats.chi(B[m is not present in stub
-[1mscipy.stats.chi2(B[m is not present in stub
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-[1mscipy.stats.circstd(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.circvar(B[m is inconsistent, stub does not have argument "keepdims"
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-[1mscipy.stats.combine_pvalues(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats.cramervonmises(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.cramervonmises(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.cramervonmises_2samp(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.cramervonmises_2samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.cramervonmises_2samp(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats.differential_entropy(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats.dweibull(B[m is not present in stub
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-[1mscipy.stats.entropy(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats.epps_singleton_2samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.epps_singleton_2samp(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats.exponnorm(B[m is not present in stub
-[1mscipy.stats.exponpow(B[m is not present in stub
-[1mscipy.stats.exponweib(B[m is not present in stub
-[1mscipy.stats.f(B[m is not present in stub
-[1mscipy.stats.f_oneway(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.f_oneway(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.fatiguelife(B[m is not present in stub
-[1mscipy.stats.fisk(B[m is not present in stub
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-[1mscipy.stats.fligner(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.fligner(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.foldcauchy(B[m is not present in stub
-[1mscipy.stats.foldnorm(B[m is not present in stub
-[1mscipy.stats.friedmanchisquare(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.friedmanchisquare(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.friedmanchisquare(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats.gausshyper(B[m is not present in stub
-[1mscipy.stats.genexpon(B[m is not present in stub
-[1mscipy.stats.genextreme(B[m is not present in stub
-[1mscipy.stats.gengamma(B[m is not present in stub
-[1mscipy.stats.genhalflogistic(B[m is not present in stub
-[1mscipy.stats.genhyperbolic(B[m is not present in stub
-[1mscipy.stats.geninvgauss(B[m is not present in stub
-[1mscipy.stats.genlogistic(B[m is not present in stub
-[1mscipy.stats.gennorm(B[m is not present in stub
-[1mscipy.stats.genpareto(B[m is not present in stub
-[1mscipy.stats.geom(B[m is not present in stub
-[1mscipy.stats.gibrat(B[m is not present in stub
-[1mscipy.stats.gmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.gmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.gompertz(B[m is not present in stub
-[1mscipy.stats.gumbel_l(B[m is not present in stub
-[1mscipy.stats.gumbel_r(B[m is not present in stub
-[1mscipy.stats.halfcauchy(B[m is not present in stub
-[1mscipy.stats.halfgennorm(B[m is not present in stub
-[1mscipy.stats.halflogistic(B[m is not present in stub
-[1mscipy.stats.halfnorm(B[m is not present in stub
-[1mscipy.stats.hmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.hmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.hypergeom(B[m is not present in stub
-[1mscipy.stats.hypsecant(B[m is not present in stub
-[1mscipy.stats.invgamma(B[m is not present in stub
-[1mscipy.stats.invgauss(B[m is not present in stub
-[1mscipy.stats.invweibull(B[m is not present in stub
-[1mscipy.stats.irwinhall(B[m is not present in stub
-[1mscipy.stats.jarque_bera(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.jarque_bera(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.jf_skew_t(B[m is not present in stub
-[1mscipy.stats.johnsonsb(B[m is not present in stub
-[1mscipy.stats.johnsonsu(B[m is not present in stub
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-[1mscipy.stats.kappa4(B[m is not present in stub
-[1mscipy.stats.kruskal(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.kruskal(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.ks_1samp(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.ks_1samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.ks_1samp(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.ks_2samp(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.ks_2samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.ks_2samp(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.ksone(B[m is not present in stub
-[1mscipy.stats.kstat(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.kstat(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.kstatvar(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.kstatvar(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.kstest(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.kstest(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.kstest(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.kstwo(B[m is not present in stub
-[1mscipy.stats.kstwobign(B[m is not present in stub
-[1mscipy.stats.kurtosis(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.kurtosistest(B[m is inconsistent, stub does not have argument "keepdims"
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-[1mscipy.stats.laplace_asymmetric(B[m is not present in stub
-[1mscipy.stats.levene(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.levene(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.levene(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.levy(B[m is not present in stub
-[1mscipy.stats.levy_l(B[m is not present in stub
-[1mscipy.stats.loggamma(B[m is not present in stub
-[1mscipy.stats.logistic(B[m is not present in stub
-[1mscipy.stats.loglaplace(B[m is not present in stub
-[1mscipy.stats.lognorm(B[m is not present in stub
-[1mscipy.stats.logser(B[m is not present in stub
-[1mscipy.stats.loguniform(B[m is not present in stub
-[1mscipy.stats.lomax(B[m is not present in stub
-[1mscipy.stats.mannwhitneyu(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.mannwhitneyu(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.maxwell(B[m is not present in stub
-[1mscipy.stats.mielke(B[m is not present in stub
-[1mscipy.stats.moment(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.mood(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.mood(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.moyal(B[m is not present in stub
-[1mscipy.stats.mstats_extras(B[m is not present in stub
-[1mscipy.stats.mvn(B[m is not present in stub
-[1mscipy.stats.nakagami(B[m is not present in stub
-[1mscipy.stats.nbinom(B[m is not present in stub
-[1mscipy.stats.ncf(B[m is not present in stub
-[1mscipy.stats.nchypergeom_fisher(B[m is not present in stub
-[1mscipy.stats.nchypergeom_wallenius(B[m is not present in stub
-[1mscipy.stats.nct(B[m is not present in stub
-[1mscipy.stats.ncx2(B[m is not present in stub
-[1mscipy.stats.nhypergeom(B[m is not present in stub
-[1mscipy.stats.norm(B[m is not present in stub
-[1mscipy.stats.normaltest(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.norminvgauss(B[m is not present in stub
-[1mscipy.stats.pareto(B[m is not present in stub
-[1mscipy.stats.pearson3(B[m is not present in stub
-[1mscipy.stats.planck(B[m is not present in stub
-[1mscipy.stats.pmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.pmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.poisson(B[m is not present in stub
-[1mscipy.stats.powerlaw(B[m is not present in stub
-[1mscipy.stats.powerlognorm(B[m is not present in stub
-[1mscipy.stats.powernorm(B[m is not present in stub
-[1mscipy.stats.randint(B[m is not present in stub
-[1mscipy.stats.ranksums(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.ranksums(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.ranksums(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.rayleigh(B[m is not present in stub
-[1mscipy.stats.rdist(B[m is not present in stub
-[1mscipy.stats.recipinvgauss(B[m is not present in stub
-[1mscipy.stats.reciprocal(B[m is not present in stub
-[1mscipy.stats.rel_breitwigner(B[m is not present in stub
-[1mscipy.stats.rice(B[m is not present in stub
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-[1mscipy.stats.rv_continuous.__init__(B[m is inconsistent, runtime argument "a" is not keyword-only
-[1mscipy.stats.rv_continuous.__init__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats.rv_continuous.__init__(B[m is inconsistent, runtime argument "xtol" is not keyword-only
-[1mscipy.stats.rv_continuous.__init__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats.rv_continuous.__init__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats.rv_continuous.__init__(B[m is inconsistent, runtime argument "longname" is not keyword-only
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-[1mscipy.stats.rv_continuous.__init__(B[m is inconsistent, runtime argument "seed" is not keyword-only
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-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, runtime argument "moment_tol" is not keyword-only
-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, stub does not have argument "values"
-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, runtime argument "inc" is not keyword-only
-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, runtime argument "shapes" is not keyword-only
-[1mscipy.stats.rv_discrete.__init__(B[m is inconsistent, runtime argument "seed" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "a" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "moment_tol" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "values" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "inc" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "shapes" is not keyword-only
-[1mscipy.stats.rv_discrete.__new__(B[m is inconsistent, runtime argument "seed" is not keyword-only
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-[1mscipy.stats.rv_discrete.logsf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
-[1mscipy.stats.rv_discrete.sf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
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-[1mscipy.stats.rv_histogram(B[m is not present in stub
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-[1mscipy.stats.sampling(B[m is not present in stub
-[1mscipy.stats.sem(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.semicircular(B[m is not present in stub
-[1mscipy.stats.shapiro(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats.shapiro(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.shapiro(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.skellam(B[m is not present in stub
-[1mscipy.stats.skew(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.skewcauchy(B[m is not present in stub
-[1mscipy.stats.skewnorm(B[m is not present in stub
-[1mscipy.stats.skewtest(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.studentized_range(B[m is not present in stub
-[1mscipy.stats.t(B[m is not present in stub
-[1mscipy.stats.tmax(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.tmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.tmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.tmin(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.trapezoid(B[m is not present in stub
-[1mscipy.stats.trapz(B[m is not present in stub
-[1mscipy.stats.triang(B[m is not present in stub
-[1mscipy.stats.truncexpon(B[m is not present in stub
-[1mscipy.stats.truncnorm(B[m is not present in stub
-[1mscipy.stats.truncpareto(B[m is not present in stub
-[1mscipy.stats.truncweibull_min(B[m is not present in stub
-[1mscipy.stats.tsem(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.tsem(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.tstd(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.tstd(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.ttest_1samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.ttest_ind(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.ttest_rel(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.tukeylambda(B[m is not present in stub
-[1mscipy.stats.tvar(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.tvar(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.uniform(B[m is not present in stub
-[1mscipy.stats.vonmises(B[m is not present in stub
-[1mscipy.stats.vonmises_line(B[m is not present in stub
-[1mscipy.stats.wald(B[m is not present in stub
-[1mscipy.stats.weibull_max(B[m is not present in stub
-[1mscipy.stats.weibull_min(B[m is not present in stub
-[1mscipy.stats.wilcoxon(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.wilcoxon(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.wrapcauchy(B[m is not present in stub
-[1mscipy.stats.yulesimon(B[m is not present in stub
-[1mscipy.stats.zipf(B[m is not present in stub
-[1mscipy.stats.zipfian(B[m is not present in stub
-[1mscipy.stats._axis_nan_policy.too_small_1d_not_omit(B[m is not present in stub
-[1mscipy.stats._axis_nan_policy.too_small_1d_omit(B[m is not present in stub
-[1mscipy.stats._axis_nan_policy.too_small_nd_not_omit(B[m is not present in stub
-[1mscipy.stats._axis_nan_policy.too_small_nd_omit(B[m is not present in stub
-[1mscipy.stats._binomtest.__all__(B[m is not present at runtime
-[1mscipy.stats._bws_test.__all__(B[m is not present at runtime
-[1mscipy.stats._censored_data.__all__(B[m is not present at runtime
-[1mscipy.stats._common.__all__(B[m is not present at runtime
-[1mscipy.stats._continuous_distns.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.stats._continuous_distns.norm_gen.fit(B[m is inconsistent, runtime does not have *args argument "args"
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-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, stub does not have *args argument "args"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "a"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "b"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "badvalue"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "longname"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "momtype"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "name"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "seed"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "shapes"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "xtol"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, stub does not have argument "density"
-[1mscipy.stats._continuous_distns.rv_histogram.__init__(B[m is inconsistent, stub does not have **kwargs argument "kwargs"
-[1mscipy.stats._discrete_distns.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.stats._discrete_distns._nchypergeom_gen.dist(B[m is not present in stub
-[1mscipy.stats._discrete_distns._nchypergeom_gen.rvs_name(B[m is not present in stub
-[1mscipy.stats._discrete_distns.nchypergeom_fisher_gen.dist(B[m is not present in stub
-[1mscipy.stats._discrete_distns.nchypergeom_fisher_gen.rvs_name(B[m is not present in stub
-[1mscipy.stats._discrete_distns.nchypergeom_wallenius_gen.dist(B[m is not present in stub
-[1mscipy.stats._discrete_distns.nchypergeom_wallenius_gen.rvs_name(B[m is not present in stub
-[1mscipy.stats._distn_infrastructure.__all__(B[m is not present at runtime
-[1mscipy.stats._distn_infrastructure.docdict(B[m is not present in stub
-[1mscipy.stats._distn_infrastructure.docdict_discrete(B[m is not present in stub
-[1mscipy.stats._distn_infrastructure.docheaders(B[m is not present in stub
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "momtype" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "a" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "xtol" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "shapes" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_continuous.__init__(B[m is inconsistent, runtime argument "seed" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "a" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "moment_tol" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, stub does not have argument "values"
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "inc" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "shapes" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__init__(B[m is inconsistent, runtime argument "seed" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "a" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "moment_tol" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "values" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "inc" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "shapes" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.__new__(B[m is inconsistent, runtime argument "seed" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_discrete.cdf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
-[1mscipy.stats._distn_infrastructure.rv_discrete.logcdf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
-[1mscipy.stats._distn_infrastructure.rv_discrete.logsf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
-[1mscipy.stats._distn_infrastructure.rv_discrete.sf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
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-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, stub does not have argument "a"
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, stub does not have argument "b"
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, runtime argument "moment_tol" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, runtime argument "values" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, runtime argument "inc" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, stub does not have argument "shapes"
-[1mscipy.stats._distn_infrastructure.rv_sample.__init__(B[m is inconsistent, runtime argument "seed" is not keyword-only
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-[1mscipy.stats._distn_infrastructure.rv_sample.b(B[m is not present at runtime
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-[1mscipy.stats._distn_infrastructure.rv_sample.qvals(B[m is not present at runtime
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-[1mscipy.stats._entropy.differential_entropy(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._entropy.entropy(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._entropy.entropy(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._hypotests.cramervonmises(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._hypotests.cramervonmises(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._hypotests.cramervonmises(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._hypotests.cramervonmises_2samp(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._hypotests.cramervonmises_2samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._hypotests.cramervonmises_2samp(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._hypotests.epps_singleton_2samp(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._hypotests.epps_singleton_2samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._hypotests.epps_singleton_2samp(B[m is inconsistent, stub does not have argument "nan_policy"
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-[1mscipy.stats._mannwhitneyu.mannwhitneyu(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.ansari(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._morestats.ansari(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.ansari(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.bartlett(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.bartlett(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.circmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.circstd(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.circvar(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.fligner(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._morestats.fligner(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.fligner(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.kstat(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.kstat(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.kstatvar(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.kstatvar(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.levene(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._morestats.levene(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.levene(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.mood(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.mood(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.shapiro(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._morestats.shapiro(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.shapiro(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._morestats.wilcoxon(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._morestats.wilcoxon(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._mstats_basic.mquantiles(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats._mstats_extras.hdquantiles(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats._mstats_extras.hdquantiles_sd(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats._mstats_extras.mjci(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats._mstats_extras.mquantiles_cimj(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats._multicomp.DecimalNumber(B[m is not present at runtime
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-[1mscipy.stats._multicomp.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_rho"
-[1mscipy.stats._multicomp.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_df"
-[1mscipy.stats._multicomp.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_std"
-[1mscipy.stats._multicomp.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_mean_samples"
-[1mscipy.stats._multicomp.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_mean_control"
-[1mscipy.stats._multicomp.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_n_samples"
-[1mscipy.stats._multicomp.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_n_control"
-[1mscipy.stats._multicomp.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_rng"
-[1mscipy.stats._multicomp.DunnettResult.__match_args__(B[m variable differs from runtime type ('statistic', 'pvalue', '_alternative', '_rho', '_df', '_std', '_mean_samples', '_mean_control', '_n_samples', '_n_control', '_rng', '_ci', '_ci_cl')
-[1mscipy.stats._multicomp.SeedType(B[m is not present at runtime
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-[1mscipy.stats._multivariate.matrix_normal_frozen.pdf(B[m is inconsistent, stub argument "x" differs from runtime argument "X"
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-[1mscipy.stats._multivariate.multivariate_t_gen.rvs(B[m is inconsistent, stub argument "shape" differs from runtime argument "df"
-[1mscipy.stats._multivariate.multivariate_t_gen.rvs(B[m is inconsistent, stub argument "shape" differs from runtime argument "size"
-[1mscipy.stats._multivariate.multivariate_t_gen.rvs(B[m is inconsistent, stub argument "df" differs from runtime argument "random_state"
-[1mscipy.stats._multivariate.multivariate_t_gen.rvs(B[m is inconsistent, runtime argument "random_state" has a default value of type None, which is incompatible with stub argument type int. This is often caused by overloads failing to account for explicitly passing in the default value.
-[1mscipy.stats._multivariate.multivariate_t_gen.rvs(B[m is inconsistent, runtime does not have argument "df"
-[1mscipy.stats._multivariate.multivariate_t_gen.rvs(B[m is inconsistent, runtime does not have argument "size"
-[1mscipy.stats._multivariate.multivariate_t_gen.rvs(B[m is inconsistent, runtime does not have argument "random_state"
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-[1mscipy.stats._multivariate.uniform_direction_frozen.rvs(B[m is inconsistent, runtime argument "size" has a default value of None, which is different from stub argument default 1
-[1mscipy.stats._multivariate.uniform_direction_gen.rvs(B[m is inconsistent, runtime argument "size" has a default value of type None, which is incompatible with stub argument type int
-[1mscipy.stats._multivariate.uniform_direction_gen.rvs(B[m is inconsistent, runtime argument "size" has a default value of None, which is different from stub argument default 1
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-[1mscipy.stats._qmc.PoissonDisk.__init__(B[m is inconsistent, runtime does not have argument "u_bounds"
-[1mscipy.stats._qmc_cy.DecimalNumber(B[m is not present at runtime
-[1mscipy.stats._qmc_cy.IntNumber(B[m is not present at runtime
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-[1mscipy.stats._resampling.MonteCarloMethod.__init__(B[m is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-[1mscipy.stats._resampling.PermutationMethod.__init__(B[m is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-[1mscipy.stats._resampling.ResamplingMethod.__init__(B[m is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-[1mscipy.stats._resampling.ResamplingMethod.batch(B[m variable differs from runtime type None
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_alternative"
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_rho"
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_df"
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_std"
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_mean_samples"
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_mean_control"
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_n_samples"
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_n_control"
-[1mscipy.stats._result_classes.DunnettResult.__init__(B[m is inconsistent, stub does not have argument "_rng"
-[1mscipy.stats._result_classes.DunnettResult.__match_args__(B[m variable differs from runtime type ('statistic', 'pvalue', '_alternative', '_rho', '_df', '_std', '_mean_samples', '_mean_control', '_n_samples', '_n_control', '_rng', '_ci', '_ci_cl')
-[1mscipy.stats._result_classes.EmpiricalDistributionFunction.__match_args__(B[m variable differs from runtime type ('quantiles', 'probabilities', '_n', '_d', '_sf', '_kind')
-[1mscipy.stats._sensitivity_analysis.SobolResult.__init__(B[m is inconsistent, stub does not have argument "_indices_method"
-[1mscipy.stats._sensitivity_analysis.SobolResult.__init__(B[m is inconsistent, stub does not have argument "_f_A"
-[1mscipy.stats._sensitivity_analysis.SobolResult.__init__(B[m is inconsistent, stub does not have argument "_f_B"
-[1mscipy.stats._sensitivity_analysis.SobolResult.__init__(B[m is inconsistent, stub does not have argument "_f_AB"
-[1mscipy.stats._sensitivity_analysis.SobolResult.__match_args__(B[m variable differs from runtime type ('first_order', 'total_order', '_indices_method', '_f_A', '_f_B', '_f_AB', '_A', '_B', '_AB', '_bootstrap_result')
-[1mscipy.stats._sobol.IntNumber(B[m is not present at runtime
-[1mscipy.stats._sobol.get_poly_vinit(B[m is not present in stub
-[1mscipy.stats._stats_mstats_common._find_repeats(B[m is not present in stub
-[1mscipy.stats._stats_py.PearsonRResultBase.__getnewargs_ex__(B[m is not present in stub
-[1mscipy.stats._stats_py.PearsonRResultBase.__init__(B[m is inconsistent, stub does not have argument "statistic"
-[1mscipy.stats._stats_py.PearsonRResultBase.__init__(B[m is inconsistent, stub does not have argument "pvalue"
-[1mscipy.stats._stats_py.PearsonRResultBase._make(B[m is not present at runtime
-[1mscipy.stats._stats_py.PearsonRResultBase._replace(B[m is not present at runtime
-[1mscipy.stats._stats_py.QuantileTestResult.__init__(B[m is inconsistent, stub does not have argument "_alternative"
-[1mscipy.stats._stats_py.QuantileTestResult.__init__(B[m is inconsistent, stub does not have argument "_x"
-[1mscipy.stats._stats_py.QuantileTestResult.__init__(B[m is inconsistent, stub does not have argument "_p"
-[1mscipy.stats._stats_py.QuantileTestResult.__match_args__(B[m variable differs from runtime type ('statistic', 'statistic_type', 'pvalue', '_alternative', '_x', '_p')
-[1mscipy.stats._stats_py.TtestResultBase.__getnewargs_ex__(B[m is not present in stub
-[1mscipy.stats._stats_py.TtestResultBase.__init__(B[m is inconsistent, stub does not have argument "statistic"
-[1mscipy.stats._stats_py.TtestResultBase.__init__(B[m is inconsistent, stub does not have argument "pvalue"
-[1mscipy.stats._stats_py.TtestResultBase._make(B[m is not present at runtime
-[1mscipy.stats._stats_py.TtestResultBase._replace(B[m is not present at runtime
-[1mscipy.stats._stats_py._SimpleChi2.cdf(B[m is not present at runtime
-[1mscipy.stats._stats_py._SimpleNormal.isf(B[m is not present at runtime
-[1mscipy.stats._stats_py.alexandergovern(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.brunnermunzel(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._stats_py.brunnermunzel(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.combine_pvalues(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.combine_pvalues(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.f_oneway(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.f_oneway(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.friedmanchisquare(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._stats_py.friedmanchisquare(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.friedmanchisquare(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.gmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.gmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.hmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.hmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.jarque_bera(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.jarque_bera(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.kruskal(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._stats_py.kruskal(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.ks_1samp(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._stats_py.ks_1samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.ks_1samp(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.ks_2samp(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._stats_py.ks_2samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.ks_2samp(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.kstest(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._stats_py.kstest(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.kstest(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.kurtosis(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.kurtosistest(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.moment(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.normaltest(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.pmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.pmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.ranksums(B[m is inconsistent, stub does not have argument "axis"
-[1mscipy.stats._stats_py.ranksums(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.ranksums(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.sem(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.skew(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.skewtest(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.tmax(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.tmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.tmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.tmin(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.tsem(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.tsem(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.tstd(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.tstd(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_py.ttest_1samp(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.ttest_ind(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.ttest_rel(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.tvar(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats._stats_py.tvar(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats._stats_pythran.__pythran__(B[m is not present in stub
-[1mscipy.stats._survival.EmpiricalDistributionFunction.__match_args__(B[m variable differs from runtime type ('quantiles', 'probabilities', '_n', '_d', '_sf', '_kind')
-[1mscipy.stats._warnings_errors.FitError.__init__(B[m is inconsistent, stub does not have argument "msg"
-[1mscipy.stats._warnings_errors.FitError.__init__(B[m is inconsistent, runtime does not have *args argument "args"
-[1mscipy.stats.distributions.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.stats.distributions.entropy(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.distributions.entropy(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "momtype" is not keyword-only
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "a" is not keyword-only
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "xtol" is not keyword-only
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "shapes" is not keyword-only
-[1mscipy.stats.distributions.rv_continuous.__init__(B[m is inconsistent, runtime argument "seed" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "a" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "moment_tol" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, stub does not have argument "values"
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "inc" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "shapes" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__init__(B[m is inconsistent, runtime argument "seed" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "a" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "b" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "name" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "badvalue" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "moment_tol" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "values" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "inc" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "longname" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "shapes" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.__new__(B[m is inconsistent, runtime argument "seed" is not keyword-only
-[1mscipy.stats.distributions.rv_discrete.cdf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
-[1mscipy.stats.distributions.rv_discrete.logcdf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
-[1mscipy.stats.distributions.rv_discrete.logsf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
-[1mscipy.stats.distributions.rv_discrete.sf(B[m is inconsistent, stub argument "x" differs from runtime argument "k"
-[1mscipy.stats.distributions.rv_frozen.dist(B[m is not present at runtime
-[1mscipy.stats.distributions.rv_frozen.moment(B[m is inconsistent, runtime argument "order" has a default value but stub argument does not
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, stub does not have argument "histogram"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, stub does not have *args argument "args"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "a"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "b"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "badvalue"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "longname"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "momtype"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "name"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "seed"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "shapes"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, runtime does not have argument "xtol"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, stub does not have argument "density"
-[1mscipy.stats.distributions.rv_histogram.__init__(B[m is inconsistent, stub does not have **kwargs argument "kwargs"
-[1mscipy.stats.mstats.BrunnerMunzelResult(B[m is not present at runtime
-[1mscipy.stats.mstats.DescribeResult(B[m is not present at runtime
-[1mscipy.stats.mstats.F_onewayResult(B[m is not present at runtime
-[1mscipy.stats.mstats.FriedmanchisquareResult(B[m is not present at runtime
-[1mscipy.stats.mstats.KruskalResult(B[m is not present at runtime
-[1mscipy.stats.mstats.KurtosistestResult(B[m is not present at runtime
-[1mscipy.stats.mstats.MannwhitneyuResult(B[m is not present at runtime
-[1mscipy.stats.mstats.ModeResult(B[m is not present at runtime
-[1mscipy.stats.mstats.NormaltestResult(B[m is not present at runtime
-[1mscipy.stats.mstats.PointbiserialrResult(B[m is not present at runtime
-[1mscipy.stats.mstats.SenSeasonalSlopesResult(B[m is not present at runtime
-[1mscipy.stats.mstats.SkewtestResult(B[m is not present at runtime
-[1mscipy.stats.mstats.Ttest_1sampResult(B[m is not present at runtime
-[1mscipy.stats.mstats.Ttest_indResult(B[m is not present at runtime
-[1mscipy.stats.mstats.Ttest_relResult(B[m is not present at runtime
-[1mscipy.stats.mstats.beta(B[m is not present at runtime
-[1mscipy.stats.mstats.binom(B[m is not present at runtime
-[1mscipy.stats.mstats.gmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.mstats.gmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.mstats.hdquantiles(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats.mstats.hdquantiles_sd(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats.mstats.hmean(B[m is inconsistent, stub does not have argument "keepdims"
-[1mscipy.stats.mstats.hmean(B[m is inconsistent, stub does not have argument "nan_policy"
-[1mscipy.stats.mstats.mjci(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats.mstats.mquantiles(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats.mstats.mquantiles_cimj(B[m is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-[1mscipy.stats.mstats.norm(B[m is not present at runtime
-[1mscipy.stats.mstats.stde_median(B[m is not present at runtime
-[1mscipy.stats.mstats.t(B[m is not present at runtime
-[1mscipy.stats.mstats.trim1(B[m is not present at runtime
-[1mscipy.stats.mstats.trimdoc(B[m is not present at runtime
-[1mscipy.stats.mstats_extras.compare_medians_ms(B[m is not present in stub
-[1mscipy.stats.mstats_extras.hdmedian(B[m is not present in stub
-[1mscipy.stats.mstats_extras.hdquantiles(B[m is not present in stub
-[1mscipy.stats.mstats_extras.hdquantiles_sd(B[m is not present in stub
-[1mscipy.stats.mstats_extras.idealfourths(B[m is not present in stub
-[1mscipy.stats.mstats_extras.median_cihs(B[m is not present in stub
-[1mscipy.stats.mstats_extras.mjci(B[m is not present in stub
-[1mscipy.stats.mstats_extras.mquantiles_cimj(B[m is not present in stub
-[1mscipy.stats.mstats_extras.rsh(B[m is not present in stub
-[1mscipy.stats.mstats_extras.trimmed_mean_ci(B[m is not present in stub
-[1mscipy.stats.qmc.PoissonDisk.__init__(B[m is inconsistent, runtime does not have argument "l_bounds"
-[1mscipy.stats.qmc.PoissonDisk.__init__(B[m is inconsistent, runtime does not have argument "u_bounds"
-[1mscipy.stats.sampling.__all__(B[m names exported from the stub do not correspond to the names exported at runtime. This is probably due to things being missing from the stub or an inaccurate `__all__` in the stub
-[1mscipy.stats.sampling.DiscreteAliasUrn(B[m is not present in stub
-[1mscipy.stats.sampling.DiscreteGuideTable(B[m is not present in stub
-[1mscipy.stats.sampling.NumericalInverseHermite(B[m is not present in stub
-[1mscipy.stats.sampling.NumericalInversePolynomial(B[m is not present in stub
-[1mscipy.stats.sampling.SimpleRatioUniforms(B[m is not present in stub
-[1mscipy.stats.sampling.TransformedDensityRejection(B[m is not present in stub
-[1mscipy.stats.sampling.UNURANError(B[m is not present in stub
-[1mscipy._lib.cobyqa.framework(B[m failed to find stubs
-[1mscipy._lib.cobyqa.main(B[m failed to find stubs
-[1mscipy._lib.cobyqa.models(B[m failed to find stubs
-[1mscipy._lib.cobyqa.problem(B[m failed to find stubs
-[1mscipy._lib.cobyqa.settings(B[m failed to find stubs
-[1mscipy._lib.cobyqa.subsolvers(B[m failed to find stubs
-[1mscipy._lib.cobyqa.subsolvers.geometry(B[m failed to find stubs
-[1mscipy._lib.cobyqa.subsolvers.optim(B[m failed to find stubs
-[1mscipy._lib.cobyqa.utils(B[m failed to find stubs
-[1mscipy._lib.cobyqa.utils.exceptions(B[m failed to find stubs
-[1mscipy._lib.cobyqa.utils.math(B[m failed to find stubs
-[1mscipy._lib.cobyqa.utils.versions(B[m failed to find stubs
-[1mscipy.fftpack.convolve(B[m failed to find stubs
-[1mscipy.linalg.cython_blas(B[m failed to find stubs
-[1mscipy.linalg.cython_lapack(B[m failed to find stubs
-[1mscipy.optimize._lsq.givens_elimination(B[m failed to find stubs
-[1mscipy.stats._levy_stable.levyst(B[m failed to find stubs
-[1mscipy.version(B[m failed to find stubs
diff --git a/tests/stubtest/allowlist.txt b/tests/stubtest/allowlist.txt
index 7c380024..8e9b7df1 100644
--- a/tests/stubtest/allowlist.txt
+++ b/tests/stubtest/allowlist.txt
@@ -72,7 +72,7 @@ scipy._lib.array_api_compat.dask.*
 scipy._lib.array_api_compat.numpy.*
 scipy._lib.array_api_compat.torch
 scipy._lib.array_api_compat.common._typing.*
-scipy._lib.cobyqa
+scipy._lib.cobyqa.*
 scipy._lib.messagestream
 scipy.fft._pocketfft.pypocketfft.*
 scipy.special.add_newdocs
@@ -80,27 +80,99 @@ scipy.special.libsf_error_state
 scipy.special._precompute.*
 scipy.special._support_alternative_backends.*
 scipy.stats._rcont.rcont
-scipy.stats._rcont.rcont
 scipy.stats._unuran.unuran_wrapper
 
-# deprecated public api
+# deprecated public api (will be removed in scipy 2.0.0)
+scipy.constants.codata
+scipy.constants.constants
+scipy.integrate.lsoda.*
+scipy.integrate.odepack.*
+scipy.integrate.quadpack.*
 scipy.integrate.quadrature
 scipy.integrate.romberg
 scipy.integrate._quadrature.quadrature
 scipy.integrate._quadrature.romberg
+scipy.linalg.basic.*
+scipy.linalg.decomp.*
+scipy.linalg.matfuncs.*
+scipy.linalg.misc.*
+scipy.linalg.special_matrices.*
+scipy.optimize.cobyla.*
+scipy.optimize.lbfgsb.*
+scipy.optimize.linesearch.*
+scipy.optimize.minpack.*
+scipy.optimize.nonlin.*
+scipy.optimize.optimize.*
+scipy.optimize.slsqp.*
+scipy.optimize.tnc.*
+scipy.optimize.zeros.*
 scipy.special.basic
 scipy.special.orthogonal
 scipy.special.sf_error
 scipy.special.specfun
 scipy.special.spfun_stats
 scipy.linalg.basic
-scipy.linalg.decomp*
 scipy.stats.kde.*
 scipy.stats.morestats.*
 scipy.stats.mstats_basic.*
+scipy.stats.mstats_extras.*
 scipy.stats.stats.*
 
 # random mypy hallucinations
 scipy.interpolate.AAA
 scipy.interpolate._aaa
 scipy.interpolate._interpnd_info
+
+# mypy didn't read the typing specs on `__all__` within stubs
+scipy._lib._ccallback.__all__
+scipy._lib.array_api_compat.__all__
+scipy._lib.array_api_compat.common.__all__
+scipy.integrate._ivp.__all__
+scipy.linalg._decomp_lu_cython.__all__
+scipy.linalg._solve_toeplitz.__all__
+scipy.optimize._constraints.__all__
+scipy.optimize._lsap.__all__
+scipy.optimize._remove_redundancy.__all__
+scipy.optimize._trlib._trlib.__all__
+scipy.signal._signaltools.__all__
+scipy.sparse._matrix.__all__
+scipy.sparse.csgraph.__all__
+scipy.sparse.csgraph._flow.__all__
+scipy.sparse.csgraph._laplacian.__all__
+scipy.sparse.csgraph._matching.__all__
+scipy.sparse.csgraph._min_spanning_tree.__all__
+scipy.sparse.csgraph._reordering.__all__
+scipy.sparse.csgraph._shortest_path.__all__
+scipy.sparse.csgraph._tools.__all__
+scipy.sparse.csgraph._traversal.__all__
+scipy.sparse.linalg._dsolve._superlu.__all__
+scipy.spatial.transform._rotation.__all__
+scipy.spatial.transform._rotation_spline.__all__
+scipy.special._add_newdocs.__all__
+scipy.special._ellip_harm.__all__
+scipy.special._lambertw.__all__
+scipy.special._orthogonal.__all__
+scipy.special._sf_error.__all__
+scipy.special._spherical_bessel.__all__
+scipy.special.cython_special.__all__
+scipy.stats._binomtest.__all__
+scipy.stats._bws_test.__all__
+scipy.stats._censored_data.__all__
+scipy.stats._common.__all__
+scipy.stats._distn_infrastructure.__all__
+scipy.stats._distr_params.__all__
+
+# mypy doesn't know how to sort `__all__`
+scipy.constants.__all__
+scipy.integrate.__all__
+scipy.linalg.__all__
+scipy.sparse.linalg._dsolve.__all__
+scipy.spatial.ckdtree.__all__
+scipy.spatial.kdtree.__all__
+scipy.spatial.qhull.__all__
+scipy.special.__all__
+scipy.stats.__all__
+scipy.stats._continuous_distns.__all__
+scipy.stats._discrete_distns.__all__
+scipy.stats.distributions.__all__
+scipy.stats.sampling.__all__