code: def hook_dummy_table_variables_to_activations(
      tpu_embedding, activations, dummy_table_variables
  ):
      """Have activations depend on dummy table variables for gradient intercept.
  
      Args:
        tpu_embedding: TPUEmbedding, activations and dummy_table_variables are from
          tpu_embedding.
        activations: An OrderedDict of feature name String to activation tensors.
        dummy_table_variables: An OrderedDict of table name String to dummy table
          variables.
  
      Returns:
        An OrderedDict of feature name String to activation tensors, which can be
          used just as the activations input.
      """
      new_activations = collections.OrderedDict()
      for feature in activations:
          table = tpu_embedding.feature_to_config_dict[feature].table_id
          new_activations[feature] = tpu_ops.tpu_embedding_activations(
              dummy_table_variables[table],
              activations[feature],
              table_id=list(tpu_embedding.table_to_config_dict).index(
                  table
              ),
              lookup_id=tpu_embedding.table_to_features_dict[
                  table
              ].index(feature),
          )
      return new_activations
  
file_path: tensorflow-upstream/tensorflow/python/tpu/tpu_embedding_gradient.py
imports: import collections
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.tpu.ops import tpu_ops
label_desc: Use image node_id 3 for calling a global function with example usage: hook_dummy_table_variables_to_activations(tpu_embedding, activations, dummy_table_variables) and returns: new_activations