code: def deserialize_torch_Tensor(header, frames):
      if header.get("grad", False):
          i = header["grad"]["start"]
          frames, grad_frames = frames[:i], frames[i:]
          grad = deserialize(header["grad"]["header"], grad_frames)
      else:
          grad = None
  
      x = deserialize(header["sub-header"], frames)
      if header["device"] == "cpu":
          t = torch.from_numpy(x)
          if header["requires_grad"]:
              t = t.requires_grad_(True)
      else:
          t = torch.tensor(
              data=x,
              device=header["device"],
              requires_grad=header["requires_grad"],
          )
      if grad is not None:
          t.grad = torch.from_numpy(grad)
      return t
  
imports: from __future__ import annotations
  import torch
  from distributed.protocol.serialize import dask_deserialize, dask_serialize, deserialize, register_generic, serialize
label_desc: Use image node_id 2 for calling a global function with example usage: deserialize_torch_Tensor(header, frames) and returns: t