@tf_export('sparse.segment_mean', v1=[])
def sparse_segment_mean_v2(data, indices, segment_ids, num_segments=None, name=None):
    "Computes the mean along sparse segments of a tensor.\n\n  Read [the section on\n  segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)\n  for an explanation of segments.\n\n  Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first\n  dimension, selecting a subset of dimension 0, specified by `indices`.\n  `segment_ids` is allowed to have missing ids, in which case the output will\n  be zeros at those indices. In those cases `num_segments` is used to determine\n  the size of the output.\n\n  Args:\n    data: A `Tensor` with data that will be assembled in the output.\n    indices: A 1-D `Tensor` with indices into `data`. Has same rank as\n      `segment_ids`.\n    segment_ids: A 1-D `Tensor` with indices into the output `Tensor`. Values\n      should be sorted and can be repeated.\n    num_segments: An optional int32 scalar. Indicates the size of the output\n      `Tensor`.\n    name: A name for the operation (optional).\n\n  Returns:\n    A `tensor` of the shape as data, except for dimension 0 which\n    has size `k`, the number of segments specified via `num_segments` or\n    inferred for the last element in `segments_ids`.\n  "
    return sparse_segment_mean(data, indices, segment_ids, name=name, num_segments=num_segments)
Computes the mean along sparse segments of a tensor.

Read [the section on
segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
for an explanation of segments.

Like `SegmentMean`, 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.
  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`.
@tf_export('sparse.segment_mean', v1=[])
def sparse_segment_mean_v2(data, indices, segment_ids, num_segments=None, name=None):
    "Computes the mean along sparse segments of a tensor.\n\n  Read [the section on\n  segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)\n  for an explanation of segments.\n\n  Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first\n  dimension, selecting a subset of dimension 0, specified by `indices`.\n  `segment_ids` is allowed to have missing ids, in which case the output will\n  be zeros at those indices. In those cases `num_segments` is used to determine\n  the size of the output.\n\n  Args:\n    data: A `Tensor` with data that will be assembled in the output.\n    indices: A 1-D `Tensor` with indices into `data`. Has same rank as\n      `segment_ids`.\n    segment_ids: A 1-D `Tensor` with indices into the output `Tensor`. Values\n      should be sorted and can be repeated.\n    num_segments: An optional int32 scalar. Indicates the size of the output\n      `Tensor`.\n    name: A name for the operation (optional).\n\n  Returns:\n    A `tensor` of the shape as data, except for dimension 0 which\n    has size `k`, the number of segments specified via `num_segments` or\n    inferred for the last element in `segments_ids`.\n  "
    return sparse_segment_mean(data, indices, segment_ids, name=name, num_segments=num_segments)