code: def upconv_layer_upsample_and_conv(
      self,
      in_filters,
      out_filters,
      scale_factor,
      kernel_size,
      stride,
      padding,
      bias,
      norm_layer,
      nonlinearity,
  ):
      parts = [
          nn.Upsample(scale_factor=scale_factor),
          nn.Conv2d(
              in_filters,
              out_filters,
              kernel_size,
              stride,
              padding=padding,
              bias=False,
              padding_mode="zeros",
          ),
      ]
  
      if norm_layer:
          parts.append(norm_layer(num_features=out_filters))
  
      if nonlinearity:
          parts.append(nonlinearity)
  
      return nn.Sequential(*parts)
  
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  from torch.nn import functional
  import torchvision
  from torchvision import models
  import pytorch_lightning
label_desc: Use image node_id 6 for calling the Generator obj's underlying member method code with example usage: obj.upconv_layer_upsample_and_conv(in_filters, out_filters, scale_factor, kernel_size, stride, padding, bias, norm_layer, nonlinearity) and returns: nn