class_docstr: Wrapper for a Tensor in :meth:`Pipe.forward` indicating that the tensor
  should not be chunked on the batch dimension and instead be replicated
  as-is across all micro-batches. This is useful for tensors which might
  not have any 'batch' semantics for the model.
class_docstr_tok: ["Wrapper","for","a","Tensor","in",":","meth",":","`","Pipe.forward","`","indicating","that","the","tensor","should","not","be","chunked","on","the","batch","dimension","and","instead","be","replicated","as-is","across","all","micro-batches",".","This","is","useful","for","tensors","which","might","not","have","any","'batch","'","semantics","for","the","model","."]
code: def tensor(self):
      return self._tensor
  
imports: import typing
  from typing import Any, Callable, List, Union, cast, Sequence
  import torch
  from torch import Tensor
  import torch.cuda.comm
label_desc: Use image node_id 2 for calling the NoChunk obj's underlying member method code with example usage: obj.tensor() and returns: self