code: def forward(self, x):
      out = self.firstconv(x)
  
      skip_connections = []
      for i in range(self.network_depth):
          out = self.encodingBlocks[i](out)
          skip_connections.append(out)
  
      for i in range(self.bottleneck_layers):
          out = self.bottleNecks[i](out)
  
      for i in range(self.network_depth):
          skip = skip_connections.pop()
          out = self.decodingBlocks[i](torch.cat([out, skip], 1))
  
      out = self.final(out)
      return out
  
file_path: pywick/pywick/models/segmentation/testnets/unet_se.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Dilated_UNet obj's underlying member method code with example usage: obj.forward(x) and returns: out