parameters: (sp_input, container=None,
                                      shared_name=None, name=None)
return_statement: return gen_sparse_ops._add_many_sparse_to_tensors_map(
        sp_input.indices, sp_input.values, sp_input.dense_shape,
        container=container, shared_name=shared_name, name=name)
docstring: Add a minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
  
    The `SparseTensor` must have rank `R` greater than 1, and the first dimension
    is treated as the minibatch dimension.  Elements of the `SparseTensor`
    must be sorted in increasing order of this first dimension.  The serialized
    `SparseTensor` objects going into each row of the output `Tensor` will have
    rank `R-1`.
  
    The minibatch size `N` is extracted from `sparse_shape[0]`.
  
    Args:
      sp_input: The input rank `R` `SparseTensor`.
      container: The container for the underlying `SparseTensorsMap` (optional).
      shared_name: The shared name for the underlying `SparseTensorsMap`
        (optional, defaults to the name of the newly created op).
      name: A name prefix for the returned tensors (optional).
  
    Returns:
      A string matrix (2-D `Tensor`) with `N` rows and `1` column.
      Each row represents a unique handle to a `SparseTensor` stored by
      the `SparseTensorMap` underlying this op.
  
    Raises:
      TypeError: If `sp_input` is not a `SparseTensor`.
docstring_summary: Add a minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
docstring_tokens: Add a minibatch SparseTensor to a SparseTensorsMap return N handles .
function: def _add_many_sparse_to_tensors_map(sp_input, container=None,
                                      shared_name=None, name=None):
    """Add a minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
  
    The `SparseTensor` must have rank `R` greater than 1, and the first dimension
    is treated as the minibatch dimension.  Elements of the `SparseTensor`
    must be sorted in increasing order of this first dimension.  The serialized
    `SparseTensor` objects going into each row of the output `Tensor` will have
    rank `R-1`.
  
    The minibatch size `N` is extracted from `sparse_shape[0]`.
  
    Args:
      sp_input: The input rank `R` `SparseTensor`.
      container: The container for the underlying `SparseTensorsMap` (optional).
      shared_name: The shared name for the underlying `SparseTensorsMap`
        (optional, defaults to the name of the newly created op).
      name: A name prefix for the returned tensors (optional).
  
    Returns:
      A string matrix (2-D `Tensor`) with `N` rows and `1` column.
      Each row represents a unique handle to a `SparseTensor` stored by
      the `SparseTensorMap` underlying this op.
  
    Raises:
      TypeError: If `sp_input` is not a `SparseTensor`.
    """
    sp_input = _convert_to_sparse_tensor(sp_input)
  
    return gen_sparse_ops._add_many_sparse_to_tensors_map(
        sp_input.indices, sp_input.values, sp_input.dense_shape,
        container=container, shared_name=shared_name, name=name)
function_tokens: def _add_many_sparse_to_tensors_map ( sp_input , container = None , shared_name = None , name = None ) : sp_input = _convert_to_sparse_tensor ( sp_input ) return gen_sparse_ops . _add_many_sparse_to_tensors_map ( sp_input . indices , sp_input . values , sp_input . dense_shape , container = container , shared_name = shared_name , name = name )