parameters: (embedding_weights,
                                   sparse_ids,
                                   sparse_weights=None,
                                   combiner="mean",
                                   default_id=None,
                                   name=None,
                                   partition_strategy="div")
return_statement: return embedding_ops.safe_embedding_lookup_sparse(
        embedding_weights,
        sparse_ids,
        sparse_weights=sparse_weights,
        combiner=combiner,
        default_id=default_id,
        name=name,
        partition_strategy=partition_strategy)
docstring: Lookup embedding results, accounting for invalid IDs and empty features.
  
    The partitioned embedding in `embedding_weights` must all be the same shape
    except for the first dimension. The first dimension is allowed to vary as the
    vocabulary size is not necessarily a multiple of `P`.
  
    Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
    with non-positive weight. For an entry with no features, the embedding vector
    for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
  
    The ids and weights may be multi-dimensional. Embeddings are always aggregated
    along the last dimension.
  
    Args:
      embedding_weights:  A list of `P` float tensors or values representing
          partitioned embedding tensors.  The total unpartitioned shape should be
          `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
          `e_1, ..., e_m` are the embedding dimensions.
      sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
          ids. `d_0` is typically batch size.
      sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
          float weights corresponding to `sparse_ids`, or `None` if all weights
          are be assumed to be 1.0.
      combiner: A string specifying how to combine embedding results for each
          entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
          the default.
      default_id: The id to use for an entry with no features.
      name: A name for this operation (optional).
      partition_strategy: A string specifying the partitioning strategy.
          Currently `"div"` and `"mod"` are supported. Default is `"div"`.
  
  
    Returns:
      Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
  
    Raises:
      ValueError: if `embedding_weights` is empty.
docstring_summary: Lookup embedding results, accounting for invalid IDs and empty features.
docstring_tokens: Lookup embedding results accounting for invalid IDs and empty features .
function: def safe_embedding_lookup_sparse(embedding_weights,
                                   sparse_ids,
                                   sparse_weights=None,
                                   combiner="mean",
                                   default_id=None,
                                   name=None,
                                   partition_strategy="div"):
    """Lookup embedding results, accounting for invalid IDs and empty features.
  
    The partitioned embedding in `embedding_weights` must all be the same shape
    except for the first dimension. The first dimension is allowed to vary as the
    vocabulary size is not necessarily a multiple of `P`.
  
    Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
    with non-positive weight. For an entry with no features, the embedding vector
    for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
  
    The ids and weights may be multi-dimensional. Embeddings are always aggregated
    along the last dimension.
  
    Args:
      embedding_weights:  A list of `P` float tensors or values representing
          partitioned embedding tensors.  The total unpartitioned shape should be
          `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
          `e_1, ..., e_m` are the embedding dimensions.
      sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
          ids. `d_0` is typically batch size.
      sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
          float weights corresponding to `sparse_ids`, or `None` if all weights
          are be assumed to be 1.0.
      combiner: A string specifying how to combine embedding results for each
          entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
          the default.
      default_id: The id to use for an entry with no features.
      name: A name for this operation (optional).
      partition_strategy: A string specifying the partitioning strategy.
          Currently `"div"` and `"mod"` are supported. Default is `"div"`.
  
  
    Returns:
      Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
  
    Raises:
      ValueError: if `embedding_weights` is empty.
    """
    return embedding_ops.safe_embedding_lookup_sparse(
        embedding_weights,
        sparse_ids,
        sparse_weights=sparse_weights,
        combiner=combiner,
        default_id=default_id,
        name=name,
        partition_strategy=partition_strategy)
function_tokens: def safe_embedding_lookup_sparse ( embedding_weights , sparse_ids , sparse_weights = None , combiner = "mean" , default_id = None , name = None , partition_strategy = "div" ) : return embedding_ops . safe_embedding_lookup_sparse ( embedding_weights , sparse_ids , sparse_weights = sparse_weights , combiner = combiner , default_id = default_id , name = name , partition_strategy = partition_strategy )