docstr: Compute the mean of values in the input data based on segment
  identifiers.
  
  Parameters
  ----------
  data : ivy.Container
      Input array or container from which to gather the input.
  segment_ids : ivy.Container
      An array of integers indicating the segment identifier for each element in
      'data'.
  num_segments : Union[int, ivy.Container]
      An integer or array representing the total number of distinct segment IDs.
  key_chains : Optional[Union[List[str], Dict[str, str], ivy.Container]], optional
      The key-chains to apply or not apply the method to. Default is None.
  to_apply : Union[bool, ivy.Container], optional
      If True, the method will be applied to key-chains, otherwise key-chains will
      be skipped. Default is True.
  prune_unapplied : Union[bool, ivy.Container], optional
      Whether to prune key-chains for which the function was not applied.
      Default is False.
  map_sequences : Union[bool, ivy.Container], optional
      Whether to also map method to sequences (lists, tuples). Default is False.
  
  Returns
  -------
  ivy.Container
      A container representing the result of a segmented mean operation.
      For each segment, it computes the mean of values in 'data' where
      'segment_ids' equals the segment ID.
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code: def static_unsorted_segment_mean(
      data: ivy.Container,
      segment_ids: Union[ivy.Array, ivy.Container],
      num_segments: Union[int, ivy.Container],
      *,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
  ) -> ivy.Container:
      """Compute the mean of values in the input data based on segment
      identifiers.
  
      Parameters
      ----------
      data : ivy.Container
          Input array or container from which to gather the input.
      segment_ids : ivy.Container
          An array of integers indicating the segment identifier for each element in
          'data'.
      num_segments : Union[int, ivy.Container]
          An integer or array representing the total number of distinct segment IDs.
      key_chains : Optional[Union[List[str], Dict[str, str], ivy.Container]], optional
          The key-chains to apply or not apply the method to. Default is None.
      to_apply : Union[bool, ivy.Container], optional
          If True, the method will be applied to key-chains, otherwise key-chains will
          be skipped. Default is True.
      prune_unapplied : Union[bool, ivy.Container], optional
          Whether to prune key-chains for which the function was not applied.
          Default is False.
      map_sequences : Union[bool, ivy.Container], optional
          Whether to also map method to sequences (lists, tuples). Default is False.
  
      Returns
      -------
      ivy.Container
          A container representing the result of a segmented mean operation.
          For each segment, it computes the mean of values in 'data' where
          'segment_ids' equals the segment ID.
      """
      return ContainerBase.cont_multi_map_in_function(
          "unsorted_segment_mean",
          data,
          segment_ids,
          num_segments,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
imports: from typing import Optional, Union, List, Dict
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 25 for calling the _ContainerWithCreationExperimental obj's underlying member method code with example usage: obj.static_unsorted_segment_mean(data, segment_ids, num_segments) and returns: ContainerBase