class_docstr: Inspired by: Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution
  https://arxiv.org/pdf/2011.11865v1.pdf
code: def forward(self, depth):
      depth = torch_nn_func.interpolate(
          depth,
          scale_factor=self.scale,
          mode="bicubic",
          align_corners=True,
      )
      features = self.conv_layers1(depth)
  
      skip_connections = {}
      skip_connections["x1"] = features
  
      for i in range(5):
          features = self.encoder_conv_blocks[i](features)
          skip_connections[f"x{(2)**(i+1)}"] = features
  
      return skip_connections
  
imports: import torch.nn.functional
  from torch import nn
  from models.mspf.blocks.multi_scale_depth import Conv2D
label_desc: Use image node_id 2 for calling the MultiscaleConvDepthEncoder obj's underlying member method code with example usage: obj.forward(depth) and returns: skip_connections