class_docstr: Multi-scale inputs
code: def forward(self, x):
      # Original
      logits = self.scale(x)
      interp = lambda l: F.interpolate(
          l, size=logits.shape[2:], mode="bilinear", align_corners=True
      )
  
      # Scaled
      logits_pyramid = []
      for p in self.pyramids:
          size = [int(s * p) for s in x.shape[2:]]
          h = F.interpolate(
              x, size=size, mode="bilinear", align_corners=True
          )
          logits_pyramid.append(self.scale(h))
  
      # Pixel-wise max
      logits_all = [logits] + [interp(l) for l in logits_pyramid]
      logits_max = torch.max(torch.stack(logits_all), dim=0)[0]
  
      if self.training:
          return [logits] + logits_pyramid + [logits_max]
      else:
          return logits_max
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the MSC obj's underlying member method code with example usage: obj.forward(x) and returns: unknown, logits_max