old_contents: import torch
  import torch.nn as nn
  
  from darts.api import Model
  from darts.modules.linear.linear_mixed_layer import MixedLayer
  
  
  class Cell(Model):
  
      def __init__(self, num_nodes, multiplier, cpp, cp, c, reduction, reduction_prev):
          """
          :param steps: 4, number of layers inside a cell
          :param multiplier: 4
          :param cpp: 48
          :param cp: 48
          :param c: 16
          :param reduction: indicates whether to reduce the output maps width
          :param reduction_prev: when previous cell reduced width, s1_d = s0_d//2
          in order to keep same shape between s1 and s0, we adopt prep0 layer to
          reduce the s0 width by half.
          """
          super(Cell, self).__init__()
  
          # indicating current cell is reduction or not
          self.reduction = reduction
          self.reduction_prev = reduction_prev
  
          # steps inside a cell
          self.num_nodes = num_nodes # 4
          self.multiplier = multiplier # 4
  
          self.layers = nn.ModuleList()
  
          for i in range(self.num_nodes):
              # for each i inside cell, it connects with all previous output
              # plus previous two cells' output
              for j in range(2 + i):
                  # for reduction cell, it will reduce the heading 2 inputs only
                  stride = 2 if reduction and j < 2 else 1
                  layer = MixedLayer(c, stride)
                  self.layers.append(layer)
  
      def forward(self, s0, s1, weights):
          """
          :param s0:
          :param s1:
          :param weights: [14, 8]
          :return:
          """
          states = [s0, s1]
          offset = 0
          # for each node, receive input from all previous intermediate nodes and s0, s1
          for i in range(self.num_nodes): # 4
              # [40, 16, 32, 32]
              #s = sum(self.layers[offset + j](h, weights[offset + j]) for j, h in enumerate(states))
              offset += len(states)
              # append one state since s is the elem-wise addition of all output
              #states.append(s)
              #print('node:',i, s.shape, self.reduction)
  
          # concat along dim=channel
          return torch.cat(states[-self.multiplier:], dim=1) # 6 of [40, 16, 32, 32]
  
new_contents: import torch
  import torch.nn as nn
  
  from darts.api import Model
  from darts.modules.linear.linear_mixed_layer import MixedLayer
  
  
  class Cell(Model):
  
      def __init__(self, num_nodes, multiplier, cpp, cp, c, reduction, reduction_prev):
          """
          :param steps: 4, number of layers inside a cell
          :param multiplier: 4
          :param cpp: 48
          :param cp: 48
          :param c: 16
          :param reduction: indicates whether to reduce the output maps width
          :param reduction_prev: when previous cell reduced width, s1_d = s0_d//2
          in order to keep same shape between s1 and s0, we adopt prep0 layer to
          reduce the s0 width by half.
          """
          super(Cell, self).__init__()
  
          # indicating current cell is reduction or not
          self.reduction = reduction
          self.reduction_prev = reduction_prev
  
          # steps inside a cell
          self.num_nodes = num_nodes # 4
          self.multiplier = multiplier # 4
  
          self.layers = nn.ModuleList()
  
          for i in range(self.num_nodes):
              # for each i inside cell, it connects with all previous output
              # plus previous two cells' output
              for j in range(2 + i):
                  # for reduction cell, it will reduce the heading 2 inputs only
                  stride = 2 if reduction and j < 2 else 1
                  layer = MixedLayer(c, stride)
                  self.layers.append(layer)
  
      def forward(self, s0, s1, weights):
          """
          :param s0:
          :param s1:
          :param weights: [14, 8]
          :return:
          """
          states = [s0, s1]
          offset = 0
          # for each node, receive input from
          # all previous intermediate nodes and s0, s1
          for i in range(self.num_nodes): # 4
              offset += len(states)
  
          # concat along dim=channel
          return torch.cat(states[-self.multiplier:], dim=1)
  
subject: Remove unnecessary comments