class_docstr: This interface contains two methods, one for moving an input across devices,
  the other one for marking streams that operate the input.
  
  torch.Tensor implements this interface and we can used it in many applications.
  Another example is torchrec.(Keyed)JaggedTensor, which we use as the input to
  torchrec.EmbeddingBagCollection, which in turn is often the first layer of many models.
  Some models take compound inputs, which should implement this interface.
docstr: Please be aware that according to https://pytorch.org/docs/stable/generated/torch.Tensor.to.html,
  `to` might return self or a copy of self.  So please remember to use `to` with the assignment operator,
  for example, `in = in.to(new_device)`.
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code: def to(
      self, device: torch.device, non_blocking: bool
  ) -> "Pipelineable":
      """
      Please be aware that according to https://pytorch.org/docs/stable/generated/torch.Tensor.to.html,
      `to` might return self or a copy of self.  So please remember to use `to` with the assignment operator,
      for example, `in = in.to(new_device)`.
      """
      ...
  
imports: import abc
  import torch
label_desc: Use image node_id 1 for calling the Pipelineable obj's underlying member method code with example usage: obj.to(device, non_blocking) without return types