class_docstr: builtin Darts Search Space
  Compared to Darts example, DartsSearchSpace removes Auxiliary Head, which
  is considered as a trick rather than part of model.
  
  Attributes
  ---
  in_channels: int
      the number of input channels
  channels: int
      the number of initial channels expected
  n_classes: int
      classes for final classification
  n_layers: int
      the number of cells contained in this network
  factory_func: function
      return a callable instance for demand cell structure.
      user should pass in ``__init__`` of the cell class with required parameters (see nni.nas.DartsCell for detail)
  n_nodes: int
      the number of nodes contained in each cell
  stem_multiplier: int
      channels multiply coefficient when passing a cell
code: def forward(self, x):
      s0 = s1 = self.stem(x)
  
      for cell in self.cells:
          s0, s1 = s1, cell(s0, s1)
  
      out = self.gap(s1)
      out = out.view(out.size(0), -1)  # flatten
      logits = self.linear(out)
  
      return logits
  
imports: import torch.nn
  from nni.nas.pytorch.search_space_zoo.darts_ops import DropPath
label_desc: Use image node_id 2 for calling the DartsStackedCells obj's underlying member method code with example usage: obj.forward(x) and returns: logits