code: def vector_to_parameters(
      vec: torch.Tensor, parameters: Iterable[torch.Tensor]
  ) -> None:
      r"""Copy slices of a vector into an iterable of parameters.
  
      Args:
          vec (Tensor): a single vector representing the parameters of a model.
          parameters (Iterable[Tensor]): an iterable of Tensors that are the
              parameters of a model.
      """
      # Ensure vec of type Tensor
      if not isinstance(vec, torch.Tensor):
          raise TypeError(
              f"expected torch.Tensor, but got: {torch.typename(vec)}"
          )
      # Flag for the device where the parameter is located
      param_device = None
  
      # Pointer for slicing the vector for each parameter
      pointer = 0
      for param in parameters:
          # Ensure the parameters are located in the same device
          param_device = _check_param_device(param, param_device)
  
          # The length of the parameter
          num_param = param.numel()
          # Slice the vector, reshape it, and replace the old data of the parameter
          param.data = (
              vec[pointer : pointer + num_param].view_as(param).data
          )
  
          # Increment the pointer
          pointer += num_param
  
imports: import torch
  from typing import Iterable, Optional
label_desc: Use image node_id 2 for calling a global function with example usage: vector_to_parameters(vec, parameters) without return types