parameters: (sample_indices_or_row_splits,
                                                   embedding_indices,
                                                   aggregation_weights,
                                                   device_ordinal,
                                                   combiners=None,
                                                   mode_override=None,
                                                   name=None)
return_statement: return gen_tpu_ops.enqueue_tpu_embedding_arbitrary_tensor_batch(
        sample_indices_or_row_splits=sample_indices_or_row_splits,
        embedding_indices=embedding_indices,
        aggregation_weights=aggregation_weights,
        device_ordinal=device_ordinal,
        combiners=combiners,
        mode_override=mode_override,
        name=name)
docstring: A placeholder op for enqueueing embedding IDs to the TPU.
  
    Args:
      sample_indices_or_row_splits: A list of rank 1 or 2 Tensors. When rank 2,
        the tensors specify the training example to which the corresponding
        embedding_indices and aggregation_weights values belong. 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. When rank 1, the tensors specify the row splits for
        splitting embedding_indices and aggregation_weights into rows. It
        corresponds to ids.row_splits in embedding_lookup(), when ids is a
        RaggedTensor. When enqueuing N-D ragged tensor, only the last dimension is
        allowed to be ragged. the row splits is 1-D dense tensor. When empty, we
        assume a dense tensor is passed to the op. 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. 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. Both float32 and float64 are allowed and will
        be converted to float32 internally.
      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.
      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 EnqueueTPUEmbeddingArbitraryTensorBatch 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_arbitrary_tensor_batch(sample_indices_or_row_splits,
                                                   embedding_indices,
                                                   aggregation_weights,
                                                   device_ordinal,
                                                   combiners=None,
                                                   mode_override=None,
                                                   name=None):
    """A placeholder op for enqueueing embedding IDs to the TPU.
  
    Args:
      sample_indices_or_row_splits: A list of rank 1 or 2 Tensors. When rank 2,
        the tensors specify the training example to which the corresponding
        embedding_indices and aggregation_weights values belong. 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. When rank 1, the tensors specify the row splits for
        splitting embedding_indices and aggregation_weights into rows. It
        corresponds to ids.row_splits in embedding_lookup(), when ids is a
        RaggedTensor. When enqueuing N-D ragged tensor, only the last dimension is
        allowed to be ragged. the row splits is 1-D dense tensor. When empty, we
        assume a dense tensor is passed to the op. 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. 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. Both float32 and float64 are allowed and will
        be converted to float32 internally.
      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.
      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 EnqueueTPUEmbeddingArbitraryTensorBatch operation.
    """
    if mode_override is None:
      mode_override = "unspecified"
    return gen_tpu_ops.enqueue_tpu_embedding_arbitrary_tensor_batch(
        sample_indices_or_row_splits=sample_indices_or_row_splits,
        embedding_indices=embedding_indices,
        aggregation_weights=aggregation_weights,
        device_ordinal=device_ordinal,
        combiners=combiners,
        mode_override=mode_override,
        name=name)
function_tokens: def enqueue_tpu_embedding_arbitrary_tensor_batch ( sample_indices_or_row_splits , embedding_indices , aggregation_weights , device_ordinal , combiners = None , mode_override = None , name = None ) : if mode_override is None : mode_override = "unspecified" return gen_tpu_ops . enqueue_tpu_embedding_arbitrary_tensor_batch ( sample_indices_or_row_splits = sample_indices_or_row_splits , embedding_indices = embedding_indices , aggregation_weights = aggregation_weights , device_ordinal = device_ordinal , combiners = combiners , mode_override = mode_override , name = name )