parameters: (data,
                          indices,
                          segment_ids,
                          name=None,
                          num_segments=None)
docstring: r"""Computes the mean 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_mean`, 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.
  
    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.
      name: A name for the operation (optional).
      num_segments: An optional int32 scalar. Indicates the size of the output
        `Tensor`.
  
    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 mean along sparse segments of a tensor.
docstring_tokens: r Computes the mean along sparse segments of a tensor .
function: def sparse_segment_mean(data,
                          indices,
                          segment_ids,
                          name=None,
                          num_segments=None):
    r"""Computes the mean 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_mean`, 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.
  
    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.
      name: A name for the operation (optional).
      num_segments: An optional int32 scalar. Indicates the size of the output
        `Tensor`.
  
    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`.
    """
    if num_segments is not None:
      return gen_math_ops.sparse_segment_mean_with_num_segments(
          data=data,
          indices=indices,
          segment_ids=segment_ids,
          num_segments=num_segments,
          name=name)
    else:
      return gen_math_ops.sparse_segment_mean(
          data=data, indices=indices, segment_ids=segment_ids, name=name)
function_tokens: def sparse_segment_mean ( data , indices , segment_ids , name = None , num_segments = None ) : if num_segments is not None : return gen_math_ops . sparse_segment_mean_with_num_segments ( data = data , indices = indices , segment_ids = segment_ids , num_segments = num_segments , name = name ) else : return gen_math_ops . sparse_segment_mean ( data = data , indices = indices , segment_ids = segment_ids , name = name )