parameters: (data,
                            indices,
                            segment_ids,
                            num_segments=None,
                            name=None)
return_statement: return sparse_segment_sum(
        data, indices, segment_ids, name=name, num_segments=num_segments)
docstring: r"""Computes the sum along sparse segments of a tensor.
  
    Read [the section on
    segmentation](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/math#about_segmentation)
    for an explanation of segments.
  
    Like `tf.math.segment_sum`, but `segment_ids` can have rank less than `data`'s
    first dimension, selecting a subset of dimension 0, specified by `indices`.
    `segment_ids` is allowed to have missing ids, in which case the output will
    be zeros at those indices. In those cases `num_segments` is used to determine
    the size of the output.
  
    For example:
  
    ```python
    c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
  
    # Select two rows, one segment.
    tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
    # => [[0 0 0 0]]
  
    # Select two rows, two segment.
    tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
    # => [[ 1  2  3  4]
    #     [-1 -2 -3 -4]]
  
    # With missing segment ids.
    tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 2]),
                          num_segments=4)
    # => [[ 1  2  3  4]
    #     [ 0  0  0  0]
    #     [-1 -2 -3 -4]
    #     [ 0  0  0  0]]
  
    # Select all rows, two segments.
    tf.sparse.segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
    # => [[0 0 0 0]
    #     [5 6 7 8]]
  
    # Which is equivalent to:
    tf.math.segment_sum(c, tf.constant([0, 0, 1]))
    ```
  
    Args:
      data: A `Tensor` with data that will be assembled in the output.
      indices: A 1-D `Tensor` with indices into `data`. Has same rank as
        `segment_ids`.
      segment_ids: A 1-D `Tensor` with indices into the output `Tensor`. Values
        should be sorted and can be repeated.
      num_segments: An optional int32 scalar. Indicates the size of the output
        `Tensor`.
      name: A name for the operation (optional).
  
    Returns:
      A `tensor` of the shape as data, except for dimension 0 which
      has size `k`, the number of segments specified via `num_segments` or
      inferred for the last element in `segments_ids`.
docstring_summary: r"""Computes the sum along sparse segments of a tensor.
docstring_tokens: r Computes the sum along sparse segments of a tensor .
function: def sparse_segment_sum_v2(data,
                            indices,
                            segment_ids,
                            num_segments=None,
                            name=None):
    r"""Computes the sum along sparse segments of a tensor.
  
    Read [the section on
    segmentation](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/math#about_segmentation)
    for an explanation of segments.
  
    Like `tf.math.segment_sum`, but `segment_ids` can have rank less than `data`'s
    first dimension, selecting a subset of dimension 0, specified by `indices`.
    `segment_ids` is allowed to have missing ids, in which case the output will
    be zeros at those indices. In those cases `num_segments` is used to determine
    the size of the output.
  
    For example:
  
    ```python
    c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
  
    # Select two rows, one segment.
    tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
    # => [[0 0 0 0]]
  
    # Select two rows, two segment.
    tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
    # => [[ 1  2  3  4]
    #     [-1 -2 -3 -4]]
  
    # With missing segment ids.
    tf.sparse.segment_sum(c, tf.constant([0, 1]), tf.constant([0, 2]),
                          num_segments=4)
    # => [[ 1  2  3  4]
    #     [ 0  0  0  0]
    #     [-1 -2 -3 -4]
    #     [ 0  0  0  0]]
  
    # Select all rows, two segments.
    tf.sparse.segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
    # => [[0 0 0 0]
    #     [5 6 7 8]]
  
    # Which is equivalent to:
    tf.math.segment_sum(c, tf.constant([0, 0, 1]))
    ```
  
    Args:
      data: A `Tensor` with data that will be assembled in the output.
      indices: A 1-D `Tensor` with indices into `data`. Has same rank as
        `segment_ids`.
      segment_ids: A 1-D `Tensor` with indices into the output `Tensor`. Values
        should be sorted and can be repeated.
      num_segments: An optional int32 scalar. Indicates the size of the output
        `Tensor`.
      name: A name for the operation (optional).
  
    Returns:
      A `tensor` of the shape as data, except for dimension 0 which
      has size `k`, the number of segments specified via `num_segments` or
      inferred for the last element in `segments_ids`.
    """
    return sparse_segment_sum(
        data, indices, segment_ids, name=name, num_segments=num_segments)
function_tokens: def sparse_segment_sum_v2 ( data , indices , segment_ids , num_segments = None , name = None ) : return sparse_segment_sum ( data , indices , segment_ids , name = name , num_segments = num_segments )