convert/move asynchronously with respect to the host if
      possible, e.g., moving CPU Tensors with pinned memory to CUDA
      devices.
  See below for examples.

  Note:

    This method modifies the module in-place.

  Parameters:
     * **device** ("torch.device") -- the desired device of the
       parameters and buffers in this module

     * **dtype** ("torch.dtype") -- the desired floating point or
       complex dtype of the parameters and buffers in this module

     * **tensor** (*torch.Tensor*) -- Tensor whose dtype and
       device are the desired dtype and device for all parameters
       and buffers in this module

     * **memory_format** ("torch.memory_format") -- the desired
       memory format for 4D parameters and buffers in this module
       (keyword only argument)

  Returns:
     self

  Return type:
     Module

  Examples:

     >>> linear = nn.Linear(2, 2)
     >>> linear.weight
