code: def test_features_unsupported_calls_are_raised():
      """
      Makes sure we raise a proper message to the user,
      when providing not supported data input or using the validator in a way that is not
      expected
      """
      validator = FeatureValidator()
      with pytest.raises(
          ValueError, match=r"Auto-sklearn does not support time"
      ):
          validator.fit(
              pd.DataFrame({"datetime": [pd.Timestamp("20180310")]})
          )
      with pytest.raises(
          ValueError,
          match=r"Auto-sklearn only supports.*yet, the provided input",
      ):
          validator.fit({"input1": 1, "input2": 2})
      validator = FeatureValidator()
      with pytest.raises(
          ValueError,
          match=r"The feature dimensionality of the train and test",
      ):
          validator.fit(
              X_train=np.array([[1, 2, 3], [4, 5, 6]]),
              X_test=np.array([[1, 2, 3, 4], [4, 5, 6, 7]]),
          )
      with pytest.raises(
          ValueError,
          match=r"Cannot call transform on a validator that is not fit",
      ):
          validator.transform(np.array([[1, 2, 3], [4, 5, 6]]))
      validator = FeatureValidator(feat_type=["Numerical"])
      with pytest.raises(
          ValueError,
          match=r"providing the option feat_type to the fit method is.*",
      ):
          validator.fit(pd.DataFrame([[1, 2, 3], [4, 5, 6]]))
      with pytest.raises(
          ValueError, match=r"feat_type does not have same number of.*"
      ):
          validator.fit(np.array([[1, 2, 3], [4, 5, 6]]))
      validator = FeatureValidator(feat_type=[1, 2, 3])
      with pytest.raises(
          ValueError, match=r"feat_type must only contain strings.*"
      ):
          validator.fit(np.array([[1, 2, 3], [4, 5, 6]]))
      validator = FeatureValidator(feat_type=["1", "2", "3"])
      with pytest.raises(
          ValueError,
          match=r"Only `Categorical`, `Numerical` and `String` are.*",
      ):
          validator.fit(np.array([[1, 2, 3], [4, 5, 6]]))
  
imports: import numpy
  import pandas
  import sklearn.datasets
  import sklearn.model_selection
  from pandas.api.types import is_categorical_dtype, is_numeric_dtype, is_string_dtype
  from scipy import sparse
  from autosklearn.data.feature_validator import FeatureValidator
  import pytest
label_desc: Use image node_id 6 for calling a global function with example usage: test_features_unsupported_calls_are_raised() without return types