class_docstr: TrainingLoop that handle inputs like python generator.
  
  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` = True.
code: def predict(
      self,
      model,
      x,
      batch_size=None,
      verbose=0,
      steps=None,
      callbacks=None,
      **kwargs,
  ):
      batch_size = model._validate_or_infer_batch_size(
          batch_size, steps, x
      )
      x, _, _ = model._standardize_user_data(
          x, check_steps=True, steps_name="steps", steps=steps
      )
      return predict_generator(
          model,
          x,
          steps=steps,
          batch_size=batch_size,
          verbose=verbose,
          workers=0,
          callbacks=callbacks,
      )
  
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 3 for calling the GeneratorLikeTrainingLoop obj's underlying member method code with example usage: obj.predict(model, x, batch_size, verbose, steps, callbacks) and returns: predict_generator