parameters: (sample_indices,
                                                embedding_indices,
                                                aggregation_weights,
                                                table_ids,
                                                device_ordinal,
                                                max_sequence_lengths=None,
                                                combiners=None,
                                                mode_override=None,
                                                name=None)
return_statement: return gen_tpu_ops.enqueue_tpu_embedding_sparse_tensor_batch(
        sample_indices=sample_indices,
        embedding_indices=embedding_indices,
        aggregation_weights=aggregation_weights,
        table_ids=table_ids,
        device_ordinal=device_ordinal,
        max_sequence_lengths=max_sequence_lengths,
        combiners=combiners,
        mode_override=mode_override,
        name=name)
docstring: A placeholder op for enqueueing embedding IDs to the TPU.
  
    Args:
      sample_indices: A list of rank 2 Tensors specifying the training example
        to which the corresponding embedding_indices and aggregation_weights
        values belong. It corresponds to sp_ids.indices in
        embedding_lookup_sparse(). If the size of its first dimension is 0, we
        assume each embedding_indices belongs to a different sample. Both int32
        and int64 are allowed and will be converted to int32 internally.
      embedding_indices: A list of rank 1 Tensors, indices into the embedding
        tables. It corresponds to sp_ids.values in embedding_lookup_sparse(). Both
        int32 and int64 are allowed and will be converted to int32 internally.
      aggregation_weights: A list of rank 1 Tensors containing per training
        example aggregation weights. It corresponds to sp_weights.values in
        embedding_lookup_sparse(). If the size of its first dimension is 0, we
        assume all weights are 1. Both float32 and float64 are allowed and will
        be converted to float32 internally.
      table_ids: A list of integers specifying the identifier of the embedding
        table (offset of TableDescriptor in the TPUEmbeddingConfiguration) to
        lookup the corresponding input. The ith input is looked up using
        table_ids[i]. The size of the table_ids list must be equal to that of
        sample_indices, embedding_indices and aggregation_weights.
      device_ordinal: The TPU device to use. Should be >= 0 and less than the
        number of TPU cores in the task on which the node is placed.
      max_sequence_lengths: A list of integers, the size of which is equal to
        sample_indices. If equal to 0, the corresponding feature is considered to
        be a non-sequence feature, If greater than 0, the corresponding feature is
        a sequence feature with the given maximal length. If None, then we assume
        a list of all zeroes.
      combiners: A list of string scalars, one for each embedding table that
        specify how to normalize the embedding activations after weighted
        summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is
        invalid to have the sum of the weights be 0 for 'mean' or the sum of the
        squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default
        is to use 'sum' for all tables (optional).
      mode_override: A string input that overrides the mode specified in the
        TPUEmbeddingConfiguration. Supported values are {'unspecified',
        'inference', 'training', 'backward_pass_only'}. When set to
        'unspecified', the mode set in TPUEmbeddingConfiguration is used,
        otherwise mode_override is used (optional).
      name: A name for the operation (optional).
  
    Returns:
      An EnqueueTPUEmbeddingSparseTensorBatch operation.
docstring_summary: A placeholder op for enqueueing embedding IDs to the TPU.
docstring_tokens: A placeholder op for enqueueing embedding IDs to the TPU .
function: def enqueue_tpu_embedding_sparse_tensor_batch(sample_indices,
                                                embedding_indices,
                                                aggregation_weights,
                                                table_ids,
                                                device_ordinal,
                                                max_sequence_lengths=None,
                                                combiners=None,
                                                mode_override=None,
                                                name=None):
    """A placeholder op for enqueueing embedding IDs to the TPU.
  
    Args:
      sample_indices: A list of rank 2 Tensors specifying the training example
        to which the corresponding embedding_indices and aggregation_weights
        values belong. It corresponds to sp_ids.indices in
        embedding_lookup_sparse(). If the size of its first dimension is 0, we
        assume each embedding_indices belongs to a different sample. Both int32
        and int64 are allowed and will be converted to int32 internally.
      embedding_indices: A list of rank 1 Tensors, indices into the embedding
        tables. It corresponds to sp_ids.values in embedding_lookup_sparse(). Both
        int32 and int64 are allowed and will be converted to int32 internally.
      aggregation_weights: A list of rank 1 Tensors containing per training
        example aggregation weights. It corresponds to sp_weights.values in
        embedding_lookup_sparse(). If the size of its first dimension is 0, we
        assume all weights are 1. Both float32 and float64 are allowed and will
        be converted to float32 internally.
      table_ids: A list of integers specifying the identifier of the embedding
        table (offset of TableDescriptor in the TPUEmbeddingConfiguration) to
        lookup the corresponding input. The ith input is looked up using
        table_ids[i]. The size of the table_ids list must be equal to that of
        sample_indices, embedding_indices and aggregation_weights.
      device_ordinal: The TPU device to use. Should be >= 0 and less than the
        number of TPU cores in the task on which the node is placed.
      max_sequence_lengths: A list of integers, the size of which is equal to
        sample_indices. If equal to 0, the corresponding feature is considered to
        be a non-sequence feature, If greater than 0, the corresponding feature is
        a sequence feature with the given maximal length. If None, then we assume
        a list of all zeroes.
      combiners: A list of string scalars, one for each embedding table that
        specify how to normalize the embedding activations after weighted
        summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is
        invalid to have the sum of the weights be 0 for 'mean' or the sum of the
        squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default
        is to use 'sum' for all tables (optional).
      mode_override: A string input that overrides the mode specified in the
        TPUEmbeddingConfiguration. Supported values are {'unspecified',
        'inference', 'training', 'backward_pass_only'}. When set to
        'unspecified', the mode set in TPUEmbeddingConfiguration is used,
        otherwise mode_override is used (optional).
      name: A name for the operation (optional).
  
    Returns:
      An EnqueueTPUEmbeddingSparseTensorBatch operation.
    """
    if mode_override is None:
      mode_override = "unspecified"
    return gen_tpu_ops.enqueue_tpu_embedding_sparse_tensor_batch(
        sample_indices=sample_indices,
        embedding_indices=embedding_indices,
        aggregation_weights=aggregation_weights,
        table_ids=table_ids,
        device_ordinal=device_ordinal,
        max_sequence_lengths=max_sequence_lengths,
        combiners=combiners,
        mode_override=mode_override,
        name=name)
function_tokens: def enqueue_tpu_embedding_sparse_tensor_batch ( sample_indices , embedding_indices , aggregation_weights , table_ids , device_ordinal , max_sequence_lengths = None , combiners = None , mode_override = None , name = None ) : if mode_override is None : mode_override = "unspecified" return gen_tpu_ops . enqueue_tpu_embedding_sparse_tensor_batch ( sample_indices = sample_indices , embedding_indices = embedding_indices , aggregation_weights = aggregation_weights , table_ids = table_ids , device_ordinal = device_ordinal , max_sequence_lengths = max_sequence_lengths , combiners = combiners , mode_override = mode_override , name = name )