class_docstr: TrainingLoop that handle inputs like array.
  
  This is the default handler for most of the input data types, includes
  symbolic tensors or Numpy array-like, Datasets and iterators in graph mode
  (since they generate symbolic tensors). This Function is used to handle model
  with `run_eagerly` = False.
class_docstr_tok: ["TrainingLoop","that","handle","inputs","like","array",".","This","is","the","default","handler","for","most","of","the","input","data","types",",","includes","symbolic","tensors","or","Numpy","array-like",",","Datasets","and","iterators","in","graph","mode","(","since","they","generate","symbolic","tensors",")",".","This","Function","is","used","to","handle","model","with","`","run_eagerly","`","=","False","."]
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,
      **kwargs,
  ):
      batch_size = model._validate_or_infer_batch_size(
          batch_size, steps_per_epoch, x
      )
  
      x, y, sample_weights = model._standardize_user_data(
          x,
          y,
          sample_weight=sample_weight,
          class_weight=class_weight,
          batch_size=batch_size,
          check_steps=True,
          steps_name="steps_per_epoch",
          steps=steps_per_epoch,
          validation_split=validation_split,
          shuffle=shuffle,
      )
  
      if validation_data:
          (
              val_x,
              val_y,
              val_sample_weights,
          ) = model._prepare_validation_data(
              validation_data, batch_size, validation_steps
          )
      elif validation_split and 0.0 < validation_split < 1.0:
          (
              x,
              y,
              sample_weights,
              val_x,
              val_y,
              val_sample_weights,
          ) = training_utils_v1.split_training_and_validation_data(
              x, y, sample_weights, validation_split
          )
      else:
          if validation_steps:
              raise ValueError(
                  "`validation_steps` should not be specified if "
                  "`validation_data` is None."
              )
          val_x, val_y, val_sample_weights = None, None, None
  
      return fit_loop(
          model,
          inputs=x,
          targets=y,
          sample_weights=sample_weights,
          batch_size=batch_size,
          epochs=epochs,
          verbose=verbose,
          callbacks=callbacks,
          val_inputs=val_x,
          val_targets=val_y,
          val_sample_weights=val_sample_weights,
          shuffle=shuffle,
          initial_epoch=initial_epoch,
          steps_per_epoch=steps_per_epoch,
          validation_steps=validation_steps,
          validation_freq=validation_freq,
          steps_name="steps_per_epoch",
      )
  
imports: import functools
  import numpy
  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.distribute import distributed_training_utils_v1
  from tensorflow.python.keras.engine import training_utils_v1
  from tensorflow.python.keras.utils.generic_utils import make_batches
  from tensorflow.python.keras.utils.generic_utils import slice_arrays
  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 ArrayLikeTrainingLoop 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) and returns: fit_loop