code: def test_loss_fn(
      x: tf.Tensor, y: tf.Tensor, weights: Optional[tf.Tensor] = None
  ) -> tf.Tensor:
      # Define a loss function which is unlikely to be coincidently defined.
      if weights is None:
          weights = 1.0
      loss = 3.14 * tf.reduce_sum(
          tf.cast(weights, tf.float32) * tf.square(x - y), axis=1
      )
      return loss
  
imports: from collections.abc import Callable, MutableSequence, Sequence
  from typing import Optional
  import numpy
  import tensorflow
  import tensorflow.compat.v2
  from tensorflow_privacy.privacy.fast_gradient_clipping import clip_grads
  from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
  from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
label_desc: Use image node_id 4 for calling a global function with example usage: test_loss_fn(x, y, weights) and returns: loss