code: def forward(self, s0, s1, drop_prob):
      s0 = self.preprocess0(s0)
      s1 = self.preprocess1(s1)
  
      states = [s0, s1]
      for i in range(self._steps):
          h1 = states[self._indices[2 * i]]
          h2 = states[self._indices[2 * i + 1]]
          op1 = self._ops[2 * i]
          op2 = self._ops[2 * i + 1]
          h1 = op1(h1)
          h2 = op2(h2)
          if self.training and drop_prob > 0.0:
              if not isinstance(op1, Identity):
                  h1 = drop_path(h1, drop_prob)
              if not isinstance(op2, Identity):
                  h2 = drop_path(h2, drop_prob)
          s = h1 + h2
          states += [s]
      return torch.cat([states[i] for i in self._concat], dim=1)
  
imports: from operations import *
  from .utils import drop_path
label_desc: Use image node_id 3 for calling the Cell obj's underlying member method code with example usage: obj.forward(s0, s1, drop_prob) and returns: torch