code: def norm_lrelu_upscale_conv_norm_lrelu(self, feat_in, feat_out):
      return nn.Sequential(
          nn.InstanceNorm3d(feat_in),
          nn.LeakyReLU(),
          nn.Upsample(scale_factor=2, mode="nearest"),
          # should be feat_in*2 or feat_in
          nn.Conv3d(
              feat_in,
              feat_out,
              kernel_size=3,
              stride=1,
              padding=1,
              bias=False,
          ),
          nn.InstanceNorm3d(feat_out),
          nn.LeakyReLU(),
      )
  
file_path: NiftyNet/demos/PyTorchNiftyNet/libs/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 5 for calling the Modified3DUNet obj's underlying member method code with example usage: obj.norm_lrelu_upscale_conv_norm_lrelu(feat_in, feat_out) and returns: nn