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 __init__(self, inp: Tensor):
      if not torch.is_tensor(inp):
          raise TypeError(
              f"NoChunk only supported for tensors, found: {inp}"
          )
      self._tensor = inp
  
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 1 to create a new NoChunk object  with example: obj = NoChunk(inp)