class_docstr: Training loop for distribution strategy with single worker.
docstr: Predict loop for Distribution Strategies.
code: def predict(
      self,
      model,
      x,
      batch_size=None,
      verbose=0,
      steps=None,
      callbacks=None,
      **kwargs,
  ):
      """Predict loop for Distribution Strategies."""
      dist_utils.validate_inputs(x=x, y=None)
      batch_size, steps = dist_utils.process_batch_and_step_size(
          model._distribution_strategy,
          x,
          batch_size,
          steps,
          ModeKeys.PREDICT,
      )
      batch_size = model._validate_or_infer_batch_size(
          batch_size, steps, x
      )
      dataset = model._distribution_standardize_user_data(
          x, batch_size=batch_size, allow_partial_batch=True
      )
      if backend.is_tpu_strategy(model._distribution_strategy):
          steps = training_utils_v1.infer_steps_for_dataset(
              model, dataset, steps, steps_name="steps"
          )
          if steps is None:
              raise ValueError(
                  "Number of steps could not be inferred from the data, "
                  "please pass the steps argument."
              )
          if not context.executing_eagerly():
              return experimental_tpu_predict_loop(
                  model,
                  dataset,
                  verbose=verbose,
                  steps=steps,
                  callbacks=callbacks,
              )
      return training_arrays_v1.predict_loop(
          model,
          dataset,
          batch_size=batch_size,
          verbose=verbose,
          steps=steps,
          callbacks=callbacks,
      )
  
imports: import numpy
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import input_lib
  from tensorflow.python.distribute import reduce_util
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import callbacks
  from tensorflow.python.keras.distribute import distribute_coordinator_utils
  from tensorflow.python.keras.distribute import distributed_training_utils_v1
  from tensorflow.python.keras.engine import partial_batch_padding_handler
  from tensorflow.python.keras.engine import training_arrays_v1
  from tensorflow.python.keras.engine import training_utils_v1
  from tensorflow.python.keras.utils.generic_utils import Progbar
  from tensorflow.python.keras.utils.mode_keys import ModeKeys
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 3 for calling the DistributionSingleWorkerTrainingLoop obj's underlying member method code with example usage: obj.predict(model, x, batch_size, verbose, steps, callbacks) and returns: training_arrays_v1, experimental_tpu_predict_loop