code: def _tpu_gather(tensor):
      def _tpu_gather_one(tensor):
          if tensor.ndim == 0:
              tensor = tensor.clone()[None]
  
          # Can only gather contiguous tensors
          if not tensor.is_contiguous():
              tensor = tensor.contiguous()
          return xm.all_gather(tensor)
  
      res = recursively_apply(
          _tpu_gather_one, tensor, error_on_other_type=True
      )
      xm.mark_step()
      return res
  
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 13 for calling a global function with example usage: _tpu_gather(tensor) and returns: res, xm