parameters: (
      emb_table: Node,
      indices: NodeInput,
      segment_ids: NodeInput,
      num_segments: Optional[NodeInput] = None,
      default_index: Optional[NodeInput] = None,
      per_sample_weights: Optional[NodeInput] = None,
      name: Optional[str] = None,
  )
return_statement: return _get_node_factory().create("EmbeddingSegmentsSum", inputs, {})
docstring: Return an EmbeddingSegmentsSum node.
  
      EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given
      input tensor with a row (from the weights matrix) at that index
  
      :param emb_table: Tensor containing the embedding lookup table.
      :param indices: Tensor with indices.
      :param segment_ids: Tensor with indices into the output Tensor
      :param num_segments: Tensor with number of segments.
      :param default_index: Scalar containing default index in embedding table to fill empty bags.
      :param per_sample_weights: Weights to be multiplied with embedding table.
      :param name: Optional name for output node.
      :return: EmbeddingSegmentsSum node
docstring_summary: Return an EmbeddingSegmentsSum node.
docstring_tokens: Return an EmbeddingSegmentsSum node .
function: def embedding_segments_sum(
      emb_table: Node,
      indices: NodeInput,
      segment_ids: NodeInput,
      num_segments: Optional[NodeInput] = None,
      default_index: Optional[NodeInput] = None,
      per_sample_weights: Optional[NodeInput] = None,
      name: Optional[str] = None,
  ) -> Node:
      """Return an EmbeddingSegmentsSum node.
  
      EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given
      input tensor with a row (from the weights matrix) at that index
  
      :param emb_table: Tensor containing the embedding lookup table.
      :param indices: Tensor with indices.
      :param segment_ids: Tensor with indices into the output Tensor
      :param num_segments: Tensor with number of segments.
      :param default_index: Scalar containing default index in embedding table to fill empty bags.
      :param per_sample_weights: Weights to be multiplied with embedding table.
      :param name: Optional name for output node.
      :return: EmbeddingSegmentsSum node
      """
      inputs = [as_node(emb_table), as_node(indices), as_node(segment_ids)]
      if per_sample_weights is not None:
          inputs.append(as_node(num_segments))
          inputs.append(as_node(default_index))
          inputs.append(as_node(per_sample_weights))
      elif default_index is not None:
          inputs.append(as_node(num_segments))
          inputs.append(as_node(default_index))
      elif num_segments is not None:
          inputs.append(as_node(num_segments))
  
      return _get_node_factory().create("EmbeddingSegmentsSum", inputs, {})
function_tokens: def embedding_segments_sum ( emb_table : Node , indices : NodeInput , segment_ids : NodeInput , num_segments : Optional [ NodeInput ] = None , default_index : Optional [ NodeInput ] = None , per_sample_weights : Optional [ NodeInput ] = None , name : Optional [ str ] = None , ) -> Node : inputs = [ as_node ( emb_table ) , as_node ( indices ) , as_node ( segment_ids ) ] if per_sample_weights is not None : inputs . append ( as_node ( num_segments ) ) inputs . append ( as_node ( default_index ) ) inputs . append ( as_node ( per_sample_weights ) ) elif default_index is not None : inputs . append ( as_node ( num_segments ) ) inputs . append ( as_node ( default_index ) ) elif num_segments is not None : inputs . append ( as_node ( num_segments ) ) return _get_node_factory ( ) . create ( "EmbeddingSegmentsSum" , inputs , { } )