code: def train(self, epochs=3, autosave=False, validate=True):
      if validate and (
          self.training_generator is None
          or self.validation_generator is None
      ):
          self.prepare_generators()
  
      base_model = Xception(
          weights="imagenet",
          include_top=False,
          input_shape=self.input_shape,
      )
  
      output = base_model.output
      output = GlobalAveragePooling2D()(output)
      output = Dense(1024, activation="relu")(output)
  
      predictions = Dense(
          len(self.training_generator.class_indices),
          activation="softmax",
      )(output)
      self.classifier = Model(
          inputs=base_model.input, outputs=predictions
      )
  
      for layer in base_model.layers:
          layer.trainable = False
  
      self.classifier.compile(
          optimizer="rmsprop",
          loss="categorical_crossentropy",
          metrics=["accuracy"],
      )
  
      callbacks = []
  
      if autosave:
          callbacks.append(
              ModelCheckpoint(
                  "datasets/context_classifier_{epoch:02d}-{val_loss:.2f}.model",
                  monitor="val_loss",
                  verbose=0,
                  save_best_only=False,
                  save_weights_only=False,
                  mode="auto",
                  period=1,
              )
          )
  
      self.classifier.fit_generator(
          self.training_generator,
          samples_per_epoch=self.training_sample_count,
          nb_epoch=epochs,
          validation_data=self.validation_generator,
          nb_val_samples=self.validation_sample_count,
          class_weight="auto",
          callbacks=callbacks,
      )
  
file_path: SerpentAI/serpent/machine_learning/context_classification/context_classifiers/cnn_xception_context_classifier.py
imports: from serpent.machine_learning.context_classification.context_classifier import ContextClassifier
  from serpent.utilities import SerpentError
  import skimage.transform
  import numpy
  import serpent.cv
label_desc: Use image node_id 2 for calling the CNNXceptionContextClassifier obj's underlying member method code with example usage: obj.train(epochs, autosave, validate) without return types