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 predict(
      self,
      model,
      x,
      batch_size=None,
      verbose=0,
      steps=None,
      callbacks=None,
      max_queue_size=10,
      workers=1,
      use_multiprocessing=False,
  ):
      model._validate_or_infer_batch_size(batch_size, steps, x)
      return predict_generator(
          model,
          x,
          steps=steps,
          verbose=verbose,
          callbacks=callbacks,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
      )
  
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 GeneratorOrSequenceTrainingLoop obj's underlying member method code with example usage: obj.predict(model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) and returns: predict_generator