code: def executable_train(
      cls,
      epochs=3,
      autosave=False,
      classifier="CNNXceptionContextClassifier",
      validate=True,
  ):
      context_paths = list()
  
      for root, directories, files in os.walk(
          "datasets/collect_frames_for_context".replace("/", os.sep)
      ):
          if root != "datasets/collect_frames_for_context".replace(
              "/", os.sep
          ):
              break
  
          for directory in directories:
              context_paths.append(
                  f"datasets/collect_frames_for_context/{directory}".replace(
                      "/", os.sep
                  )
              )
  
      if not len(context_paths):
          raise ContextClassifierError(
              "No Context Frames found in 'datasets/collect_frames_for_datasets'..."
          )
  
      serpent.datasets.create_training_and_validation_sets(
          context_paths
      )
  
      context_classifier_class = cls.context_classifier_mapping().get(
          classifier
      )
  
      if context_classifier_class is not None:
          context_path = random.choice(context_paths)
          frame_path = None
  
          for root, directories, files in os.walk(context_path):
              for file in files:
                  if file.endswith(".png"):
                      frame_path = f"{context_path}/{file}"
                      break
  
              if frame_path is not None:
                  break
  
          frame = skimage.io.imread(frame_path)
  
          context_classifier = context_classifier_class(
              input_shape=frame.shape
          )
  
          context_classifier.train(
              epochs=epochs, autosave=autosave, validate=validate
          )
          context_classifier.validate()
  
          context_classifier.save_classifier(
              "datasets/context_classifier.model"
          )
          print(
              "Success! Model was saved to 'datasets/context_classifier.model'"
          )
  
file_path: SerpentAI/serpent/machine_learning/context_classification/context_classifier.py
imports: import serpent.datasets
  import os
  import random
  import skimage.io
label_desc: Use image node_id 11 for calling the ContextClassifier obj's underlying member method code with example usage: obj.executable_train(cls, epochs, autosave, classifier, validate) without return types