class_docstr: The full search space proposed by `NAS-Bench-101 <http://proceedings.mlr.press/v97/ying19a/ying19a.pdf>`__.
  
  It's simply a stack of :class:`~nni.nas.hub.pytorch.modules.NasBench101Cell`. Operations are conv3x3, conv1x1 and maxpool respectively.
  
  Parameters
  ----------
  stem_out_channels
      Number of output channels of the stem convolution.
  num_stacks
      Number of stacks in the network.
  num_modules_per_stack
      Number of modules in each stack. Each module is a :class:`~nni.nas.nn.pytorch.NasBench101Cell`.
  max_num_vertices
      Maximum number of vertices in each cell.
  max_num_edges
      Maximum number of edges in each cell.
  num_labels
      Number of categories for classification.
  bn_eps
      Epsilon for batch normalization.
  bn_momentum
      Momentum for batch normalization.
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code: def forward(self, x):
      bs = x.size(0)
      out = self.stem_conv(x)
      for layer in self.features:
          out = layer(out)
      out = self.gap(out).view(bs, -1)
      out = self.classifier(out)
      return out
  
imports: import math
  import torch
  import torch.nn
  from nni.nas.nn.pytorch import ModelSpace
  from .modules.nasbench101 import NasBench101Cell
label_desc: Use image node_id 2 for calling the NasBench101 obj's underlying member method code with example usage: obj.forward(x) and returns: out