code: def send_tpu_embedding_gradients(
      inputs, config, learning_rates=None, name=None
  ):
      """A placeholder op for feeding per-sample gradients to the embedding layer.
  
      Args:
        inputs: A TensorList of gradients with which to update embedding tables.
          This argument has the same length and shapes as the return value of
          RecvTPUEmbeddingActivations, but contains gradients of the model's loss
          with respect to the embedding activations. The embedding tables are
          updated from these gradients via the optimizers specified in the TPU
          embedding configuration given to tpu.initialize_system.
        config: Serialized TPUEmbeddingConfiguration proto.
        learning_rates: A TensorList of float32 scalars, one for each dynamic
            learning rate tag: see the comments in
              //third_party/tensorflow/core/protobuf/tpu/
              optimization_parameters.proto. Multiple tables can share the same
              dynamic learning rate tag as specified in the configuration. If the
              learning rates for all tables are constant, this list should be empty.
        name: A name for the operation (optional).
  
      Returns:
        A SendTPUEmbeddingGradients operation.
      """
      if learning_rates is None:
          learning_rates = []
      return gen_tpu_ops.send_tpu_embedding_gradients(
          inputs=inputs,
          learning_rates=learning_rates,
          config=config,
          name=name,
      )
  
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_tpu_ops
  from tensorflow.python.ops.gen_tpu_ops import *
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.tpu import tpu_function
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 11 for calling a global function with example usage: send_tpu_embedding_gradients(inputs, config, learning_rates, name) and returns: gen_tpu_ops