code: def forward(self, x):
      x_in = torch.cat(x, dim=1) if isinstance(x, tuple) else x
      if self.has_proj:
          if self.key_stride == 2:
              x_s = self.c1x1_w_s2(x_in)
          else:
              x_s = self.c1x1_w_s1(x_in)
          x_s1 = x_s[:, : self.num_1x1_c, :, :]
          x_s2 = x_s[:, self.num_1x1_c :, :, :]
      else:
          x_s1 = x[0]
          x_s2 = x[1]
      x_in = self.c1x1_a(x_in)
      x_in = self.c3x3_b(x_in)
      if self.b:
          x_in = self.c1x1_c(x_in)
          out1 = self.c1x1_c1(x_in)
          out2 = self.c1x1_c2(x_in)
      else:
          x_in = self.c1x1_c(x_in)
          out1 = x_in[:, : self.num_1x1_c, :, :]
          out2 = x_in[:, self.num_1x1_c :, :, :]
      resid = x_s1 + out1
      dense = torch.cat([x_s2, out2], dim=1)
      return resid, dense
  
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
  from collections import OrderedDict
  from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
  from .convert_from_mxnet import convert_from_mxnet, has_mxnet
label_desc: Use image node_id 2 for calling the DualPathBlock obj's underlying member method code with example usage: obj.forward(x) and returns: resid,  dense