class_docstr: Cell structure that is proposed in NAS-Bench-201.
  
  Proposed by `NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search <https://arxiv.org/abs/2001.00326>`__.
  
  This cell is a densely connected DAG with ``num_tensors`` nodes, where each node is tensor.
  For every :math:`i < j`, there is an edge from i-th node to j-th node.
  Each edge in this DAG is associated with an operation transforming the hidden state from the source node
  to the target node. All possible operations are selected from a predefined operation set, defined in ``op_candidates``.
  Each of the ``op_candidates`` should be a callable that accepts input dimension and output dimension,
  and returns a ``Module``.
  
  Input of this cell should be of shape :math:`[N, C_{in}, *]`, while output should be :math:`[N, C_{out}, *]`. For example,
  
  The space size of this cell would be :math:`|op|^{N(N-1)/2}`, where :math:`|op|` is the number of operation candidates,
  and :math:`N` is defined by ``num_tensors``.
  
  Parameters
  ----------
  op_candidates : list of callable
      Operation candidates. Each should be a function accepts input feature and output feature, returning nn.Module.
  in_features : int
      Input dimension of cell.
  out_features : int
      Output dimension of cell.
  num_tensors : int
      Number of tensors in the cell (input included). Default: 4
  label : str
      Identifier of the cell. Cell sharing the same label will semantically share the same choice.
code: def label(self) -> str:
      return self._scope.name
  
file_path: nni/nni/nas/hub/pytorch/modules/nasbench201.py
imports: from collections import OrderedDict
  from typing import Callable, List, Dict, Union, Optional
  import torch
  import torch.nn
  from nni.mutable import label_scope
  from nni.nas.nn.pytorch import LayerChoice, MutableModule
label_desc: Use image node_id 3 for calling the NasBench201Cell obj's underlying member method code with example usage: obj.label() and returns: self