code: def unsorted_segment_mean(
      data: torch.Tensor,
      segment_ids: torch.Tensor,
      num_segments: Union[int, torch.Tensor],
  ) -> torch.Tensor:
      ivy.utils.assertions.check_unsorted_segment_valid_params(
          data, segment_ids, num_segments
      )
  
      # Initialize an array to store the sum of elements for each segment
      segment_sum = torch.zeros(
          (num_segments,) + data.shape[1:],
          dtype=data.dtype,
          device=data.device,
      )
  
      # Initialize an array to keep track of the number of elements in each segment
      counts = torch.zeros(
          num_segments, dtype=torch.int64, device=data.device
      )
  
      for i in range(len(segment_ids)):
          seg_id = segment_ids[i]
          segment_sum[seg_id] += data[i]
          counts[seg_id] += 1
  
      return segment_sum / counts[:, None]
  
file_path: ivy/ivy/functional/backends/torch/experimental/creation.py
imports: from typing import Optional, Tuple, Union
  import math
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ..None import backend_version
label_desc: Use image node_id 11 for calling a global function with example usage: unsorted_segment_mean(data, segment_ids, num_segments) and returns: unknown