class_docstr: Generator-like.
  
  Input is Python generator, or Sequence object.
  
  The difference between this class and `GeneratorLikeTrainingFunction` is that
  this class only handles inputs that with x, y and sample_weight fused into one
  param.
code: def fit(
      self,
      model,
      x=None,
      y=None,
      batch_size=None,
      epochs=1,
      verbose=1,
      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_freq=1,
      max_queue_size=10,
      workers=1,
      use_multiprocessing=False,
  ):
      model._validate_or_infer_batch_size(
          batch_size, steps_per_epoch, x
      )
      training_utils_v1.check_generator_arguments(
          y, sample_weight, validation_split=validation_split
      )
      return fit_generator(
          model,
          x,
          steps_per_epoch=steps_per_epoch,
          epochs=epochs,
          verbose=verbose,
          callbacks=callbacks,
          validation_data=validation_data,
          validation_steps=validation_steps,
          validation_freq=validation_freq,
          class_weight=class_weight,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          shuffle=shuffle,
          initial_epoch=initial_epoch,
          steps_name="steps_per_epoch",
      )
  
imports: import functools
  import math
  import numpy
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import callbacks
  from tensorflow.python.keras.engine import training_utils
  from tensorflow.python.keras.engine import training_utils_v1
  from tensorflow.python.keras.utils import data_utils
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.keras.utils.mode_keys import ModeKeys
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.types import data
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 for calling the GeneratorOrSequenceTrainingLoop obj's underlying member method code with example usage: obj.fit(model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing) and returns: fit_generator