code: def train_step(self, data):
      # Unpack the data. Its structure depends on your model and
      # on what you pass to `fit()`.
      x, y = data
  
      with tf.GradientTape() as tape:
          y_pred = self(x, training=True)  # Forward pass
          # Compute the loss value
          # (the loss function is configured in `compile()`)
          loss = self.compute_loss(y=y, y_pred=y_pred)
  
      # Compute gradients
      trainable_vars = self.trainable_variables
      gradients = tape.gradient(loss, trainable_vars)
  
      # Update weights
      self.optimizer.apply(gradients, trainable_vars)
  
      # 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)
  
      # Return a dict mapping metric names to current value
      return {m.name: m.result() for m in self.metrics}
  
imports: import os
  import tensorflow
  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