code: def _gpu_gather(tensor):
      state = PartialState()
      if is_torch_version(">=", "1.13"):
          gather_op = torch.distributed.all_gather_into_tensor
      else:
          gather_op = torch.distributed._all_gather_base
  
      def _gpu_gather_one(tensor):
          if tensor.ndim == 0:
              tensor = tensor.clone()[None]
  
          # Can only gather contiguous tensors
          if not tensor.is_contiguous():
              tensor = tensor.contiguous()
  
          if state.backend is not None and state.backend != "gloo":
              # We use `empty` as `all_gather_into_tensor` slightly
              # differs from `all_gather` for better efficiency,
              # and we rely on the number of items in the tensor
              # rather than its direct shape
              output_tensors = torch.empty(
                  state.num_processes * tensor.numel(),
                  dtype=tensor.dtype,
                  device=state.device,
              )
              gather_op(output_tensors, tensor)
              return output_tensors.view(-1, *tensor.size()[1:])
          else:
              # a backend of `None` is always CPU
              # also gloo does not support `all_gather_into_tensor`,
              # which will result in a larger memory overhead for the op
              output_tensors = [
                  torch.empty_like(tensor)
                  for _ in range(state.num_processes)
              ]
              torch.distributed.all_gather(output_tensors, tensor)
              return torch.cat(output_tensors, dim=0)
  
      return recursively_apply(
          _gpu_gather_one, tensor, error_on_other_type=True
      )
  
imports: import pickle
  import warnings
  from functools import update_wrapper, wraps
  from typing import Any, Mapping
  import torch
  from ..state import PartialState
  from .constants import TORCH_DISTRIBUTED_OPERATION_TYPES
  from .dataclasses import DistributedType, TensorInformation
  from .imports import is_npu_available, is_torch_distributed_available, is_torch_version, is_tpu_available
label_desc: Use image node_id 14 for calling a global function with example usage: _gpu_gather(tensor) and returns: recursively_apply, output_tensors, torch