parameters: (inputs,
                                   config,
                                   learning_rates=None,
                                   name=None)
return_statement: return gen_tpu_ops.send_tpu_embedding_gradients(
        inputs=inputs, learning_rates=learning_rates, config=config, name=name)
docstring: 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.
docstring_summary: A placeholder op for feeding per-sample gradients to the embedding layer.
docstring_tokens: A placeholder op for feeding per - sample gradients to the embedding layer .
function: 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)
function_tokens: def send_tpu_embedding_gradients ( inputs , config , learning_rates = None , name = None ) : 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 )