code: def test_isotonic_calibration_bad_input_type():
      ic = IsotonicCalibration()
  
      x_train = [[1, 1], [2, 3.5]]
      y_train = [[0.9, 0.1], [0.2, 0.8]]
  
      with pytest.raises(ValueError):
          ic.fit(x_train=None, y_train=None)
  
      # Test mixed types
      with pytest.raises(ValueError):
          ic.fit(x_train=None, y_train=np.array(y_train))
  
      # Test mixed types
      with pytest.raises(ValueError):
          ic.fit(x_train=np.array(x_train), y_train=None)
  
      with pytest.raises(
          ValueError
      ):  # y_train should be np.ndarray not list
          ic.fit(x_train=np.array(x_train), y_train=y_train)
  
      with pytest.raises(
          ValueError
      ):  # x_train should be np.ndarray not list
          ic.fit(x_train=x_train, y_train=np.array(y_train))
  
      # some tests for the predict method where the input data have the wrong
      # dimensionality or type
      # first call fit
      ic.fit(x_train=np.array(x_train), y_train=np.array(y_train))
      # Now predict a new point
      x_test = [[1]]
  
      with pytest.raises(ValueError):
          ic.predict(x=x_test)  # wrong type
  
      with pytest.raises(ValueError):
          ic.predict(x=np.array(x_test))
  
file_path: stellargraph/tests/test_calibration.py
imports: import pytest
  import numpy
  from stellargraph.calibration import *
label_desc: Use image node_id 3 for calling a global function with example usage: test_isotonic_calibration_bad_input_type() without return types