code: def custom_reduced_loss(y_batch, y_pred):
      del y_batch
      # Create a loss multiplier to avoid small gradients.
      large_value_multiplier = 1e10
      sqr_outputs = []
      for t in y_pred:
          reduction_axes = tf.range(1, len(t.shape))
          sqr_outputs.append(
              tf.reduce_sum(tf.square(t), axis=reduction_axes)
          )
      sqr_tsr = tf.stack(sqr_outputs, axis=1)
      return large_value_multiplier * tf.reduce_sum(sqr_tsr, axis=1)
  
file_path: privacy/tensorflow_privacy/privacy/fast_gradient_clipping/bert_encoder_utils_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_models
  from tensorflow_privacy.privacy.fast_gradient_clipping import bert_encoder_utils
label_desc: Use image node_id 4 for calling a global function with example usage: custom_reduced_loss(y_batch, y_pred) and returns: unknown