code: def segment_sum(
      data: torch.Tensor, segment_ids: torch.Tensor
  ) -> torch.Tensor:
      """This function computes the sum of values along segments within a tensor. It is useful when you have a tensor with segment IDs and you want to compute the sum of values for each segment.
      This function is analogous to tf.segment_sum. (https://www.tensorflow.org/api_docs/python/tf/math/segment_sum).
  
      Parameters
      ----------
      data: torch.Tensor
          A pytorch tensor containing the values to be summed. It can have any shape, but its rank (number of dimensions) should be at least 1.
      segment_ids: torch.Tensor
          A 1-D tensor containing the indices for the segmentation. The segments can be any non-negative integer values, but they must be sorted in non-decreasing order.
  
      Returns
      -------
      out_tensor: torch.Tensor
          Tensor with the same shape as data, where each value corresponds to the sum of values within the corresponding segment.
  
      Examples
      --------
      >>> data = torch.Tensor([[1, 2, 3, 4], [4, 3, 2, 1], [5, 6, 7, 8]])
      >>> segment_ids = torch.Tensor([0, 0, 1]).to(torch.int64)
      >>> result = segment_sum(data=data, segment_ids=segment_ids)
      >>> data.shape[0]
      3
      >>> segment_ids.shape[0]
      3
      >>> len(segment_ids.shape)
      1
      >>> result
      tensor([[5., 5., 5., 5.],
              [5., 6., 7., 8.]])
  
      """
      if not all(
          segment_ids[i] <= segment_ids[i + 1]
          for i in range(len(segment_ids) - 1)
      ):
          raise AssertionError("elements of segment_ids must be sorted")
  
      if len(segment_ids.shape) != 1:
          raise AssertionError("segment_ids have be a 1-D tensor")
  
      if data.shape[0] != segment_ids.shape[0]:
          raise AssertionError(
              "segment_ids should be the same size as dimension 0 of input."
          )
  
      num_segments = len(torch.unique(segment_ids))
      out_tensor = unsorted_segment_sum(data, segment_ids, num_segments)
  
      return out_tensor
  
file_path: deepchem/deepchem/utils/pytorch_utils.py
imports: import scipy
  import torch
  from typing import Callable, Union, List, Generator, Tuple
label_desc: Use image node_id 3 for calling a global function with example usage: segment_sum(data, segment_ids) and returns: out_tensor