code: def test_step(self, data):
      # Unpack the data
      x, y = data
      # Compute predictions
      y_pred = self(x, training=False)
      # Updates the metrics tracking the loss
      loss = self.compute_loss(y=y, y_pred=y_pred)
      # Update the metrics.
      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.
      # 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.test_step(data) and returns: unknown