code: def set_return_type(return_type: str):
      """Set the return type of torch operations on :class:`~torchvision.tv_tensors.TVTensor`.
  
      This only affects the behaviour of torch operations. It has no effect on
      ``torchvision`` transforms or functionals, which will always return as
      output the same type that was passed as input.
  
      .. warning::
  
          We recommend using :class:`~torchvision.transforms.v2.ToPureTensor` at
          the end of your transform pipelines if you use
          ``set_return_type("TVTensor")``. This will avoid the
          ``__torch_function__`` overhead in the models ``forward()``.
  
      Can be used as a global flag for the entire program:
  
      .. code:: python
  
          img = tv_tensors.Image(torch.rand(3, 5, 5))
          img + 2  # This is a pure Tensor (default behaviour)
  
          set_return_type("TVTensor")
          img + 2  # This is an Image
  
      or as a context manager to restrict the scope:
  
      .. code:: python
  
          img = tv_tensors.Image(torch.rand(3, 5, 5))
          img + 2  # This is a pure Tensor
          with set_return_type("TVTensor"):
              img + 2  # This is an Image
          img + 2  # This is a pure Tensor
  
      Args:
          return_type (str): Can be "TVTensor" or "Tensor" (case-insensitive).
              Default is "Tensor" (i.e. pure :class:`torch.Tensor`).
      """
      global _TORCHFUNCTION_SUBCLASS
      to_restore = _TORCHFUNCTION_SUBCLASS
  
      try:
          _TORCHFUNCTION_SUBCLASS = {"tensor": False, "tvtensor": True}[
              return_type.lower()
          ]
      except KeyError:
          raise ValueError(
              f"return_type must be 'TVTensor' or 'Tensor', got {return_type}"
          ) from None
  
      return _ReturnTypeCM(to_restore)
  
imports: import torch
label_desc: Use image node_id 1 for calling a global function with example usage: set_return_type(return_type) and returns: _ReturnTypeCM