parameters: (sp_input, axis=None, keepdims=None,
                        reduction_axes=None, keep_dims=None)
return_statement: return gen_sparse_ops.sparse_reduce_sum(
        sp_input.indices, sp_input.values, sp_input.dense_shape,
        math_ops._ReductionDims(sp_input, axis), keepdims)
docstring: Computes `tf.sparse.add` of elements across dimensions of a SparseTensor.
  
    This is the reduction operation for the elementwise `tf.sparse.add` op.
  
    This Op takes a SparseTensor and is the sparse counterpart to
    `tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`
    instead of a sparse one.
  
    Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
    `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in
    `reduction_axes`. If `keepdims` is true, the reduced dimensions are retained
    with length 1.
  
    If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
    with a single element is returned.  Additionally, the axes can be negative,
    similar to the indexing rules in Python.
  
    For example:
  
      # 'x' represents [[1, ?, 1]
      #                 [?, 1, ?]]
      # where ? is implicitly-zero.
  
      >>> x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 1, 1], [2, 3])
      >>> tf.sparse.reduce_sum(x)
      <tf.Tensor: shape=(), dtype=int32, numpy=3>
      >>> tf.sparse.reduce_sum(x, 0)
      <tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 1, 1], dtype=int32)>
      >>> tf.sparse.reduce_sum(x, 1)  # Can also use -1 as the axis
      <tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 1], dtype=int32)>
      >>> tf.sparse.reduce_sum(x, 1, keepdims=True)
      <tf.Tensor: shape=(2, 1), dtype=int32, numpy=
      array([[2],
             [1]], dtype=int32)>
      >>> tf.sparse.reduce_sum(x, [0, 1])
      <tf.Tensor: shape=(), dtype=int32, numpy=3>
  
    Args:
      sp_input: The SparseTensor to reduce. Should have numeric type.
      axis: The dimensions to reduce; list or scalar. If `None` (the
        default), reduces all dimensions.
      keepdims: If true, retain reduced dimensions with length 1.
      reduction_axes: Deprecated name of `axis`.
      keep_dims: Deprecated alias for `keepdims`.
  
    Returns:
      The reduced Tensor.
docstring_summary: Computes `tf.sparse.add` of elements across dimensions of a SparseTensor.
docstring_tokens: Computes tf . sparse . add of elements across dimensions of a SparseTensor .
function: def sparse_reduce_sum(sp_input, axis=None, keepdims=None,
                        reduction_axes=None, keep_dims=None):
    """Computes `tf.sparse.add` of elements across dimensions of a SparseTensor.
  
    This is the reduction operation for the elementwise `tf.sparse.add` op.
  
    This Op takes a SparseTensor and is the sparse counterpart to
    `tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`
    instead of a sparse one.
  
    Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
    `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in
    `reduction_axes`. If `keepdims` is true, the reduced dimensions are retained
    with length 1.
  
    If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
    with a single element is returned.  Additionally, the axes can be negative,
    similar to the indexing rules in Python.
  
    For example:
  
      # 'x' represents [[1, ?, 1]
      #                 [?, 1, ?]]
      # where ? is implicitly-zero.
  
      >>> x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 1, 1], [2, 3])
      >>> tf.sparse.reduce_sum(x)
      <tf.Tensor: shape=(), dtype=int32, numpy=3>
      >>> tf.sparse.reduce_sum(x, 0)
      <tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 1, 1], dtype=int32)>
      >>> tf.sparse.reduce_sum(x, 1)  # Can also use -1 as the axis
      <tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 1], dtype=int32)>
      >>> tf.sparse.reduce_sum(x, 1, keepdims=True)
      <tf.Tensor: shape=(2, 1), dtype=int32, numpy=
      array([[2],
             [1]], dtype=int32)>
      >>> tf.sparse.reduce_sum(x, [0, 1])
      <tf.Tensor: shape=(), dtype=int32, numpy=3>
  
    Args:
      sp_input: The SparseTensor to reduce. Should have numeric type.
      axis: The dimensions to reduce; list or scalar. If `None` (the
        default), reduces all dimensions.
      keepdims: If true, retain reduced dimensions with length 1.
      reduction_axes: Deprecated name of `axis`.
      keep_dims: Deprecated alias for `keepdims`.
  
    Returns:
      The reduced Tensor.
    """
    keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
                                                      "keep_dims", keep_dims)
    axis = deprecation.deprecated_argument_lookup("axis", axis, "reduction_axes",
                                                  reduction_axes)
    if keepdims is None:
      keepdims = False
  
    return gen_sparse_ops.sparse_reduce_sum(
        sp_input.indices, sp_input.values, sp_input.dense_shape,
        math_ops._ReductionDims(sp_input, axis), keepdims)
function_tokens: def sparse_reduce_sum ( sp_input , axis = None , keepdims = None , reduction_axes = None , keep_dims = None ) : keepdims = deprecation . deprecated_argument_lookup ( "keepdims" , keepdims , "keep_dims" , keep_dims ) axis = deprecation . deprecated_argument_lookup ( "axis" , axis , "reduction_axes" , reduction_axes ) if keepdims is None : keepdims = False return gen_sparse_ops . sparse_reduce_sum ( sp_input . indices , sp_input . values , sp_input . dense_shape , math_ops . _ReductionDims ( sp_input , axis ) , keepdims )