code: def enqueue_tpu_embedding_sparse_batch(
      sample_indices,
      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: A list of rank 1 Tensors specifying the training example and
          feature to which the corresponding embedding_indices and
          aggregation_weights values belong. sample_indices[i] must equal b * nf +
          f, where nf is the number of features from the corresponding table, f is
          in [0, nf), and b is in [0, batch size). 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 sample -- i.e.,
          per (training example, feature) -- 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', 'train', '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 EnqueueTPUEmbeddingSparseBatch operation.
      """
      if mode_override is None:
          mode_override = "unspecified"
      return gen_tpu_ops.enqueue_tpu_embedding_sparse_batch(
          sample_indices=sample_indices,
          embedding_indices=embedding_indices,
          aggregation_weights=aggregation_weights,
          device_ordinal=device_ordinal,
          combiners=combiners,
          mode_override=mode_override,
          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 13 for calling a global function with example usage: enqueue_tpu_embedding_sparse_batch(sample_indices, embedding_indices, aggregation_weights, device_ordinal, combiners, mode_override, name) and returns: gen_tpu_ops