class_docstr: Unit tests for the NNModifier class. 
class_docstr_tok: ["Unit","tests","for","the","NNModifier","class","."]
docstr: Tests the __call__ function with a single input of the modifier. 
docstr_tok: ["Tests","the","__call__","function","with","a","single","input","of","the","modifier","."]
code: def test_call(self):
      """Tests the __call__ function with a single input of the modifier."""
      self.report(
          "Testing the __call__ function with single input of the modifier."
      )
      num_modifications = 20
      num_steps_probs = [0.5, 0.25, 0.125, 0.075, 0.05]
      save_dir = os.path.join(self.save_dir, "modifier_call_single")
      if os.path.exists(save_dir):
          rmtree(save_dir)
      test_nns = self.cnns + self.mlps
      for idx, old_nn in enumerate(test_nns):
          save_prefix = str(idx)
          modifier, constraint_checker = self._get_modifier_and_cc(
              old_nn
          )
          get_modifications = lambda arg_nn: modifier(
              arg_nn, num_modifications, num_steps_probs
          )
          run_orig_vs_modifications(
              save_dir,
              save_prefix,
              old_nn,
              get_modifications,
              constraint_checker,
              self.report,
          )
  
imports: from copy import deepcopy
  import numpy
  import os
  from shutil import rmtree
  import unittest
  from .None import nn_domains
  from .None import nn_modifiers
  from .neural_network import NeuralNetwork
  from .nn_visualise import visualise_nn
  from .unittest_neural_network import generate_cnn_architectures, generate_mlp_architectures
  from ..utils.base_test_class import BaseTestClass, execute_tests
label_desc: Use image node_id 6 for calling the NNModifierTestCase obj's underlying member method code with example usage: obj.test_call() without return types