code: def __init__(self, input_channels, filters, downsample=True):
      super().__init__()
      self.conv_res = torch.nn.Conv2d(
          input_channels, filters, 1, stride=(2 if downsample else 1)
      )
  
      self.net = torch.nn.Sequential(
          torch.nn.Conv2d(input_channels, filters, 3, padding=1),
          torch.nn.LeakyReLU(0.2, inplace=True),
          torch.nn.Conv2d(filters, filters, 3, padding=1),
          torch.nn.LeakyReLU(0.2, inplace=True),
      )
  
      self.downsample = (
          torch.nn.Sequential(
              Blur(),
              torch.nn.Conv2d(filters, filters, 3, padding=1, stride=2),
          )
          if downsample
          else None
      )
  
imports: import math
  from functools import partial
  from math import log2
  from typing import List
  import torch
  import torch.nn.functional
  from einops import rearrange
  from kornia.filters import filter2d
  from nemo.collections.tts.parts.utils.helpers import mask_sequence_tensor
label_desc: Use image node_id 1 to create a new DiscriminatorBlock object from inherited base classes: torch.nn with example: obj = DiscriminatorBlock(input_channels, filters, downsample)