code: def relu_layer(
      self,
      in_filters,
      out_filters,
      kernel_size,
      stride,
      padding,
      bias,
      norm_layer,
      nonlinearity,
  ):
      out = nn.Sequential()
      out.add_module(
          "conv",
          nn.Conv2d(
              in_channels=in_filters,
              out_channels=out_filters,
              kernel_size=kernel_size,
              stride=stride,
              padding=padding,
              bias=bias,
              padding_mode="zeros",
          ),
      )
  
      if norm_layer:
          out.add_module(
              "normalization", norm_layer(num_features=out_filters)
          )
      if nonlinearity:
          out.add_module("nonlinearity", nonlinearity)
      # out.add_module('dropout', nn.Dropout2d(0.25))
  
      return out
  
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 4 for calling the Generator obj's underlying member method code with example usage: obj.relu_layer(in_filters, out_filters, kernel_size, stride, padding, bias, norm_layer, nonlinearity) and returns: out