code: def make_train_function(self, force=False):
      if self.train_function is not None and not force:
          return self.train_function
  
      if self.steps_per_execution > 1:
          raise ValueError(
              "`steps_per_execution` must be 1 with the PyTorch backend. "
              f"Received: steps_per_execution={self.steps_per_execution}"
          )
  
      def one_step_on_data(data):
          """Runs a single training step on a batch of data."""
          data = data[0]
          return self.train_step(data)
  
      if self._should_torch_compile():
          self.train_function = torch.compile(one_step_on_data)
      else:
          self.train_function = one_step_on_data
  
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 6 for calling the TorchTrainer obj's underlying member method code with example usage: obj.make_train_function(force) and returns: self, self