parameters: (indices, dim, size, shards=1, name='embedding')
return_statement: return shared_embedding_lookup(params, indices)
docstring: Make an embedding layer that dedupes across multiple index tensors.
  
    Each value in any of the ids will be looked up once.
  
    Args:
      indices: List of integer id tensors, each of any shape.
      dim: Embedding dimension.
      size: Vocabulary size.
      shards: Number of embedding matrix shards.
      name: Name for the embedding variables.
  
    Returns:
      List of embedding results.  The ith result has shape
        `indices[i].shape + (dim,)`.
docstring_summary: Make an embedding layer that dedupes across multiple index tensors.
docstring_tokens: Make an embedding layer that dedupes across multiple index tensors .
function: def shared_embedding_layer(indices, dim, size, shards=1, name='embedding'):
    """Make an embedding layer that dedupes across multiple index tensors.
  
    Each value in any of the ids will be looked up once.
  
    Args:
      indices: List of integer id tensors, each of any shape.
      dim: Embedding dimension.
      size: Vocabulary size.
      shards: Number of embedding matrix shards.
      name: Name for the embedding variables.
  
    Returns:
      List of embedding results.  The ith result has shape
        `indices[i].shape + (dim,)`.
    """
    params = embedding_weights(dim=dim, size=size, shards=shards, name=name)
    return shared_embedding_lookup(params, indices)
function_tokens: def shared_embedding_layer ( indices , dim , size , shards = 1 , name = 'embedding' ) : params = embedding_weights ( dim = dim , size = size , shards = shards , name = name ) return shared_embedding_lookup ( params , indices )