code: def forward(self, x, e=None):
      # x = F.upsample(x, scale_factor=2, mode='bilinear', align_corners=True)
      x = self.deconv(x)
      if e is not None:
          x = torch.cat([x, e], 1)
      x = self.bn(x)
  
      x = F.relu(self.conv1(x), inplace=True)
      x = F.relu(self.conv2(x), inplace=True)
  
      g1 = self.spatial_gate(x)
      g2 = self.channel_gate(x)
      x = x * g1 + x * g2
  
      return x
  
imports: from torch import nn
  from torch.nn import functional
  import torch
  from torchvision import models
  from torchvision.models import resnet34, resnet101, resnet50, resnet152
  import torchvision
label_desc: Use image node_id 2 for calling the DecoderBlockV5 obj's underlying member method code with example usage: obj.forward(x, e) and returns: x