code: def fit(
      self,
      x=None,
      y=None,
      batch_size=None,
      epochs=1,
      verbose="auto",
      callbacks=None,
      validation_split=0.0,
      validation_data=None,
      shuffle=True,
      class_weight=None,
      sample_weight=None,
      initial_epoch=0,
      steps_per_epoch=None,
      validation_steps=None,
      validation_batch_size=None,
      validation_freq=1,
  ):
      self._assert_compile_called("fit")
      # TODO: respect compiled trainable state
      self._eval_epoch_iterator = None
      if validation_split and validation_data is None:
          # Create the validation data using the training data. Only supported
          # for TF/numpy/jax arrays.
          (
              x,
              y,
              sample_weight,
          ), validation_data = data_adapter_utils.train_validation_split(
              (x, y, sample_weight), validation_split=validation_split
          )
  
      if validation_data:
          (
              val_x,
              val_y,
              val_sample_weight,
          ) = data_adapter_utils.unpack_x_y_sample_weight(
              validation_data
          )
  
      # Create an iterator that yields batches for one epoch.
      epoch_iterator = TFEpochIterator(
          x=x,
          y=y,
          sample_weight=sample_weight,
          batch_size=batch_size,
          steps_per_epoch=steps_per_epoch,
          shuffle=shuffle,
          class_weight=class_weight,
          distribute_strategy=self.distribute_strategy,
          steps_per_execution=self.steps_per_execution,
      )
  
      # Container that configures and calls callbacks.
      if not isinstance(callbacks, callbacks_module.CallbackList):
          callbacks = callbacks_module.CallbackList(
              callbacks,
              add_history=True,
              add_progbar=verbose != 0,
              verbose=verbose,
              epochs=epochs,
              steps=epoch_iterator.num_batches,
              model=self,
          )
  
      self.stop_training = False
      self.make_train_function()
      callbacks.on_train_begin()
      training_logs = None
      logs = None
      for epoch in range(initial_epoch, epochs):
          self.reset_metrics()
          callbacks.on_epoch_begin(epoch)
          with epoch_iterator.catch_stop_iteration():
              for step, iterator in epoch_iterator.enumerate_epoch():
                  callbacks.on_train_batch_begin(step)
                  logs = self.train_function(iterator)
                  callbacks.on_train_batch_end(
                      step, self._pythonify_logs(logs)
                  )
                  if self.stop_training:
                      break
  
          # Override with model metrics instead of last step logs
          epoch_logs = self.get_metrics_result()
  
          # Run validation.
          if validation_data and self._should_eval(
              epoch, validation_freq
          ):
              # Create EpochIterator for evaluation and cache it.
              if getattr(self, "_eval_epoch_iterator", None) is None:
                  self._eval_epoch_iterator = TFEpochIterator(
                      x=val_x,
                      y=val_y,
                      sample_weight=val_sample_weight,
                      batch_size=validation_batch_size or batch_size,
                      distribute_strategy=self.distribute_strategy,
                      steps_per_execution=self.steps_per_execution,
                      steps_per_epoch=validation_steps,
                      shuffle=False,
                  )
              val_logs = self.evaluate(
                  x=val_x,
                  y=val_y,
                  sample_weight=val_sample_weight,
                  batch_size=validation_batch_size or batch_size,
                  steps=validation_steps,
                  callbacks=callbacks,
                  return_dict=True,
                  _use_cached_eval_dataset=True,
              )
              val_logs = {
                  "val_" + name: val for name, val in val_logs.items()
              }
              epoch_logs.update(val_logs)
  
          callbacks.on_epoch_end(epoch, epoch_logs)
          training_logs = epoch_logs
          if self.stop_training:
              break
  
      if (
          isinstance(self.optimizer, optimizers_module.Optimizer)
          and epochs > 0
      ):
          self.optimizer.finalize_variable_values(
              self.trainable_weights
          )
  
      # If _eval_epoch_iterator exists, delete it after all epochs are done.
      if getattr(self, "_eval_epoch_iterator", None) is not None:
          del self._eval_epoch_iterator
      callbacks.on_train_end(logs=training_logs)
      return self.history
  
imports: import contextlib
  import warnings
  import numpy
  import tensorflow
  import tree
  from packaging.version import Version
  from tensorflow.python.eager import context
  from keras import callbacks
  from keras import metrics
  from keras import optimizers
  from keras.trainers import trainer
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.trainers.epoch_iterator import EpochIterator
  from keras.utils import traceback_utils
label_desc: Use image node_id 11 for calling the TensorFlowTrainer obj's underlying member method code with example usage: obj.fit(x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq) and returns: self