code: def train_step(self, data):
      # Unpack the data. Its structure depends on your model and
      # on what you pass to `fit()`.
      if len(data) == 3:
          x, y, sample_weight = data
      else:
          sample_weight = None
          x, y = data
  
      # Call torch.nn.Module.zero_grad() to clear the leftover gradients
      # for the weights from the previous train step.
      self.zero_grad()
  
      # Compute loss
      y_pred = self(x, training=True)  # Forward pass
      loss = self.compute_loss(
          y=y,
          y_pred=y_pred,
          sample_weight=sample_weight,
      )
  
      # Call torch.Tensor.backward() on the loss to compute gradients
      # for the weights.
      loss.backward()
  
      trainable_weights = [v for v in self.trainable_weights]
      gradients = [v.value.grad for v in trainable_weights]
  
      # Update weights
      with torch.no_grad():
          self.optimizer.apply(gradients, trainable_weights)
  
      # Update metrics (includes the metric that tracks the loss)
      for metric in self.metrics:
          if metric.name == "loss":
              metric.update_state(loss)
          else:
              metric.update_state(
                  y, y_pred, sample_weight=sample_weight
              )
  
      # Return a dict mapping metric names to current value
      # Note that it will include the loss (tracked in self.metrics).
      return {m.name: m.result() for m in self.metrics}
  
imports: import os
  import torch
  import keras
  from keras import layers
  import numpy
label_desc: Use image node_id 1 for calling the CustomModel obj's underlying member method code with example usage: obj.train_step(data) and returns: unknown