code: def serialize_torch_Tensor(t):
      requires_grad_ = t.requires_grad
  
      if requires_grad_:
          sub_header, frames = serialize(t.detach().numpy())
      else:
          sub_header, frames = serialize(t.numpy())
  
      header = {"sub-header": sub_header}
      if t.grad is not None:
          grad_header, grad_frames = serialize(t.grad.numpy())
          header["grad"] = {"header": grad_header, "start": len(frames)}
          frames += grad_frames
      header["requires_grad"] = requires_grad_
      header["device"] = t.device.type
      return header, frames
  
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 1 for calling a global function with example usage: serialize_torch_Tensor(t) and returns: header,  frames