code: def gather_all_tensors(
      result: torch.Tensor, group: Optional[Any] = None
  ) -> List[torch.Tensor]:
      """Function to gather all tensors from several processes onto a list that is broadcast to all processes.
  
      Works on tensors that have the same number of dimensions, but where each dimension may differ. In this case
      tensors are padded, gathered and then trimmed to secure equal workload for all processes.
  
      :param result: the value to sync
      :param group: the process group to gather results from (not supported: always uses world)
  
      :return: list with size equal to the process group where gathered_result[i]
               corresponds to result tensor from process i
      """
      if group is not None:
          raise ValueError(
              "Horovod does not support allgather using a subcommunicator at this time. "
              "Unset `group`."
          )
  
      if _HVD is None or not _HVD.is_initialized():
          return [result]
  
      if len(result.shape) == 0:
          # Convert scalars to single dimension tensors
          result = result.reshape(1)
  
      is_bool = False
      if result.dtype == torch.bool:
          # need to convert to int due to Horovod limitation
          result = result.int()
          is_bool = True
  
      # Add extra dimension to the tensors to be gathered
      result = result.unsqueeze(0)
  
      # sync and gather all
      gathered_result = _HVD.allgather(result)
      # This is to match the output of the torchmetrics gather_all_tensors function
      # and ensures that the return value is usable by torchmetrics.compute downstream.
      # Ensures that the output is a list of tensors.
      gathered_result = list(gathered_result)
  
      if is_bool:
          # convert back if needed
          gathered_result = [t.bool() for t in gathered_result]
  
      return gathered_result
  
imports: import os
  from typing import Any, List, Optional
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: gather_all_tensors(result, group) and returns: gathered_result, list