diff --git a/python/paddle/pir/math_op_patch.py b/python/paddle/pir/math_op_patch.py
index f12603c56c5c7..8d43e53c95239 100644
--- a/python/paddle/pir/math_op_patch.py
+++ b/python/paddle/pir/math_op_patch.py
@@ -477,18 +477,53 @@ def _T_(self):
         return _C_ops.transpose(self, perm)
 
     def _int_(self):
-        raise TypeError(
-            "int(Value) is not supported in static graph mode. If you are using @to_static, you can try this:\n"
-            "1. If you want to get the value of Value, you can switch to non-fullgraph mode by setting @to_static(full_graph=True).\n"
-            "2. If you want to run it in full graph mode, you need use Value.astype(paddle.int32), and do not use int(Value)."
-        )
+        error_msg = """\
+            int(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode.
+            It's usually triggered by the logging implicitly, for example:
+                >>> logging.info("The value of x is: {int(x)}")
+                                                          ^ `x` is Tensor, `int(x)` triggers int(Tensor)
+
+                There are two common workarounds available:
+                If you are logging Tensor values, then consider logging only at dynamic graphs, for example:
+
+                    Modify the following code
+                    >>> logging.info("The value of x is: {int(x)}")
+                    to
+                    >>> if paddle.in_dynamic_mode():
+                    ...     logging.info("The value of x is: {int(x)}")
+
+                If you need to convert the Tensor type, for example:
+                    Modify the following code
+                    >>> x = int(x)
+                    to
+                    >>> x = x.astype("int64")
+        """
+
+        raise TypeError(textwrap.dedent(error_msg))
 
     def _float_(self):
-        raise TypeError(
-            "float(Value) is not supported in static graph mode. If you are using @to_static, you can try this:\n"
-            "1. If you want to get the value of Value, you can switch to non-fullgraph mode by setting @to_static(full_graph=True).\n"
-            "2. If you want to run it in full graph mode, you need use Value directly, and do not use float(Value)."
-        )
+        error_msg = """\
+            float(Tensor) is not supported in static graph mode. Because it's value is not available during the static mode.
+            It's usually triggered by the logging implicitly, for example:
+                >>> logging.info("The value of x is: {float(x)}")
+                                                            ^ `x` is Tensor, `float(x)` triggers float(Tensor)
+
+                There are two common workarounds available:
+                If you are logging Tensor values, then consider logging only at dynamic graphs, for example:
+
+                    Modify the following code
+                    >>> logging.info("The value of x is: {float(x)}")
+                    to
+                    >>> if paddle.in_dynamic_mode():
+                    ...     logging.info("The value of x is: {float(x)}")
+
+                If you need to convert the Tensor type, for example:
+                    Modify the following code
+                    >>> x = float(x)
+                    to
+                    >>> x = x.astype("float64")
+        """
+        raise TypeError(textwrap.dedent(error_msg))
 
     def _bool_(self):
         error_msg = """\