code: def predict(
      self,
      x,
      batch_size=None,
      verbose="auto",
      steps=None,
      callbacks=None,
  ):
      # Create an iterator that yields batches of input data.
      epoch_iterator = TorchEpochIterator(
          x=x,
          batch_size=batch_size,
          steps_per_epoch=steps,
          shuffle=False,
          steps_per_execution=self.steps_per_execution,
      )
  
      # Container that configures and calls callbacks.
      if not isinstance(callbacks, callbacks_module.CallbackList):
          callbacks = callbacks_module.CallbackList(
              callbacks,
              add_history=True,
              add_progbar=verbose != 0,
              verbose=verbose,
              epochs=1,
              steps=epoch_iterator.num_batches,
              model=self,
          )
  
      def append_to_outputs(batch_outputs, outputs):
          if outputs is None:
              outputs = tree.map_structure(
                  lambda batch_output: [batch_output],
                  batch_outputs,
              )
          else:
              tree.map_structure_up_to(
                  batch_outputs,
                  lambda output, batch_output: output.append(
                      batch_output
                  ),
                  outputs,
                  batch_outputs,
              )
          return outputs
  
      # Switch the torch Module back to testing mode.
      self.eval()
  
      self.make_predict_function()
      self.stop_predicting = False
      callbacks.on_predict_begin()
      outputs = None
      for step, data in epoch_iterator.enumerate_epoch():
          callbacks.on_predict_batch_begin(step)
          batch_outputs = self.predict_function(data)
          outputs = append_to_outputs(batch_outputs, outputs)
          callbacks.on_predict_batch_end(
              step, {"outputs": batch_outputs}
          )
          if self.stop_predicting:
              break
      callbacks.on_predict_end()
      outputs = tree.map_structure(backend.convert_to_numpy, outputs)
      return tree.map_structure_up_to(
          batch_outputs, np.concatenate, outputs
      )
  
imports: import collections
  import itertools
  import warnings
  import numpy
  import torch
  import tree
  from packaging.version import parse
  from keras import backend
  from keras import callbacks
  from keras import optimizers
  from keras.trainers import data_adapters
  from keras.trainers import trainer
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.trainers.epoch_iterator import EpochIterator
  from keras.utils import traceback_utils
label_desc: Use image node_id 11 for calling the TorchTrainer obj's underlying member method code with example usage: obj.predict(x, batch_size, verbose, steps, callbacks) and returns: tree, outputs