code: def train_step(self, data):
      x, y, sample_weight = data_adapter_utils.unpack_x_y_sample_weight(
          data
      )
  
      # Compute predictions
      if self._call_has_training_arg:
          y_pred = self(x, training=True)
      else:
          y_pred = self(x)
  
      # Call torch.nn.Module.zero_grad() to clear the leftover gradients
      # for the weights from the previous train step.
      self.zero_grad()
  
      loss = self.compute_loss(
          x=x, y=y, y_pred=y_pred, sample_weight=sample_weight
      )
      self._loss_tracker.update_state(loss)
      if self.optimizer is not None:
          loss = self.optimizer.scale_loss(loss)
  
      # Compute gradients
      if self.trainable_weights:
          # Call torch.Tensor.backward() on the loss to compute gradients
          # for the weights.
          loss.backward()
  
          trainable_weights = self.trainable_weights[:]
          gradients = [v.value.grad for v in trainable_weights]
  
          # Update weights
          with torch.no_grad():
              self.optimizer.apply(gradients, trainable_weights)
      else:
          warnings.warn(
              "The model does not have any trainable weights."
          )
  
      return self.compute_metrics(
          x, y, y_pred, sample_weight=sample_weight
      )
  
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 3 for calling the TorchTrainer obj's underlying member method code with example usage: obj.train_step(data) and returns: self