code: def make_block(
      self, flt_in, flt_out, k, stride, padding, bias, norm, relu
  ):
      m = nn.Sequential()
      m.add_module(
          "conv",
          nn.Conv2d(
              flt_in,
              flt_out,
              k,
              stride=stride,
              padding=padding,
              bias=bias,
              padding_mode="zeros",
          ),
      )
      if norm is not None:
          m.add_module("norm", norm(flt_out))
      if relu is not None:
          m.add_module("relu", relu)
      return m
  
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 3 for calling the Discriminator obj's underlying member method code with example usage: obj.make_block(flt_in, flt_out, k, stride, padding, bias, norm, relu) and returns: m