code: def all_reverse(tensor, world_size, field, clip, dim=0):
      if world_size > 1:
          tensor = (
              tensor.float()
          )  # tensors must be on cpu and float for all_gather
          all_tensors = [
              torch.zeros_like(tensor) for _ in range(world_size)
          ]
          torch.distributed.barrier()  # all_gather is experimental for gloo, found that these barriers were necessary
          torch.distributed.all_gather(all_tensors, tensor)
          torch.distributed.barrier()
          tensor = torch.cat(
              all_tensors, 0
          ).long()  # tensors must be long for reverse
      # for distributed training, dev sets are padded with extra examples so that the
      # tensors are all of a predictable size for all_gather. This line removes those extra examples
      return field.reverse(tensor)[:clip]
  
file_path: decanlp/validate.py
imports: import torch
  from util import pad
  from metrics import compute_metrics
label_desc: Use image node_id 3 for calling a global function with example usage: all_reverse(tensor, world_size, field, clip, dim) and returns: field