Not reporting all extra columns in dataframe

[mashby1966]

The validation of a dataframe is not reporting all of the columns that exist in the dataframe but do not exist in the validation schema (ie extra columns)
The following code reports only that column2 is extra.

BTW Note that it is correctly reporting that column1 and column3 do not exist in the dataframe but are required in the schema (ie missing columns)

import pandas as pd
import pandera as pa

from pandera import Column, DataFrameSchema,errors

schema = pa.DataFrameSchema(
    {
    "column1": Column(int,required=True),
    "column3": Column(int,required=True)
    },
    strict=True)

df = pd.DataFrame({"column2": [1, 2, 3],
                   "column4": [4,5,6]})

print(df.to_markdown())
try:
    schema.validate(df,lazy=True)
except errors.SchemaErrors as exc:
    print(exc.failure_cases)

The output is

|    |   column2 |   column4 |
|---:|----------:|----------:|
|  0 |         1 |         4 |
|  1 |         2 |         5 |
|  2 |         3 |         6 |
    schema_context column                check check_number failure_case index
0  DataFrameSchema   None     column_in_schema         None      column2  None
1  DataFrameSchema   None  column_in_dataframe         None      column1  None
2  DataFrameSchema   None  column_in_dataframe         None      column3  None

Is this expected behaviour when lazy=True?