class_docstr: Atrous spatial pyramid pooling used in object detection and segmentation.
  
  Args:
      in_channels (`int`):
          Number of input channels.
      depth (`int`):
          Hidden dimension.
code: def forward(self, x):
      size = x.shape[2:]
  
      image_features = self.mean(x)
      image_features = self.conv(image_features)
      image_features = F.upsample(
          image_features, size=size, mode="bilinear"
      )
  
      atrous_block1 = self.atrous_block1(x)
  
      atrous_block6 = self.atrous_block6(x)
  
      atrous_block12 = self.atrous_block12(x)
  
      atrous_block18 = self.atrous_block18(x)
  
      net = self.conv_1x1_output(
          torch.cat(
              [
                  image_features,
                  atrous_block1,
                  atrous_block6,
                  atrous_block12,
                  atrous_block18,
              ],
              dim=1,
          )
      )
      return net
  
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the ASPP obj's underlying member method code with example usage: obj.forward(x) and returns: net