code: def __init__(
      self,
      num_nodes,
      in_channels_pp,
      in_channels_p,
      out_channels,
      reduction,
  ):
      super().__init__()
      self.preproc0 = Calibration(in_channels_pp, out_channels)
      self.preproc1 = Calibration(in_channels_p, out_channels)
  
      self.num_nodes = num_nodes
      name_prefix = "reduce" if reduction else "normal"
      self.nodes = nn.ModuleList()
      node_labels = [
          mutables.InputChoice.NO_KEY,
          mutables.InputChoice.NO_KEY,
      ]
      for i in range(num_nodes):
          node_labels.append("{}_node_{}".format(name_prefix, i))
          self.nodes.append(
              Node(node_labels[-1], node_labels[:-1], out_channels)
          )
      self.final_conv_w = nn.Parameter(
          torch.zeros(
              out_channels, self.num_nodes + 2, out_channels, 1, 1
          ),
          requires_grad=True,
      )
      self.bn = nn.BatchNorm2d(out_channels, affine=False)
      self.reset_parameters()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from nni.nas.pytorch import mutables
  from ops import FactorizedReduce, StdConv, SepConvBN, Pool
label_desc: Use image node_id 1 to create a new ENASLayer object from inherited base classes: nn with example: obj = ENASLayer(num_nodes, in_channels_pp, in_channels_p, out_channels, reduction)