code: def convert_tensor(tensor: torch.Tensor, clone: bool) -> torch.Tensor:
      tensor = tensor.detach().cpu()
      if isinstance(tensor, torch.HalfTensor):
          # We convert any fp16 params to fp32 to make sure operations like
          # division by a scalar value are supported.
          tensor = tensor.float()
      elif clone:
          # tensor.float() would have effectively cloned the fp16 tensor already,
          # so we don't need to do it again even if clone=True.
          tensor = tensor.clone()
      return tensor
  
imports: import os
  from collections import deque, OrderedDict
  from typing import Any, Deque, Dict, List, Optional, Tuple
  import torch
  from fairseq import checkpoint_utils, utils
  from pytorch_translate import constants
  from pytorch_translate.file_io import PathManager
label_desc: Use image node_id 7 for calling a global function with example usage: convert_tensor(tensor, clone) and returns: tensor