text: import torch
  from torch import nn
  
  
  class Conv2dBlock(nn.Module):
      def __init__(self, in_c, out_c, ker_size=(3, 3), stride=1, padding=1, batch_norm=True, relu=True):
          super(Conv2dBlock, self).__init__()
          self.conv2d = nn.Conv2d(in_c, out_c, ker_size, stride, padding)
          if batch_norm:
              self.batch_norm = nn.BatchNorm2d(out_c, eps=1e-5, momentum=0.05, affine=True)
          if relu:
              self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x):
          for module in self.children():
              x = module(x)
          return x
  
  
  class ConvTranspose2dBlock(nn.Module):
      def __init__(self, in_c, out_c, ker_size=(3, 3), stride=1, padding=1, batch_norm=True, relu=True):
          super(ConvTranspose2dBlock, self).__init__()
          self.convtranpose2d = nn.ConvTranspose2d(in_c, out_c, ker_size, stride, padding)
          if batch_norm:
              self.batch_norm = nn.BatchNorm2d(out_c, eps=1e-5, momentum=0.05, affine=True)
          if relu:
              self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x):
          for module in self.children():
              x = module(x)
          return x
  
  
  class DownSampleBlock(nn.Module):
      def __init__(self, ker_size=(2, 2), stride=2, dilation=(1, 1), ceil_mode=False, p=0.0):
          super(DownSampleBlock, self).__init__()
          self.maxpooling = nn.MaxPool2d(kernel_size=ker_size, stride=stride, dilation=dilation, ceil_mode=ceil_mode)
          if p:
              self.dropout = nn.Dropout(p)
  
      def forward(self, x):
          for module in self.children():
              x = module(x)
          return x
  
  
  class UpSampleBlock(nn.Module):
      def __init__(
          self, in_c, out_c, kernel_size, stride, padding, scale_factor=(2, 2), mode="bilinear", batch_norm=True, p=0.0
      ):
          super(UpSampleBlock, self).__init__()
          self.upsample = nn.Upsample(scale_factor=scale_factor, mode=mode)
          self.conv2d = nn.Conv2d(in_c, out_c, kernel_size=kernel_size, stride=stride, padding=padding)
          if batch_norm:
              self.batch_norm = nn.BatchNorm2d(out_c, eps=1e-5, momentum=0.05, affine=True)
          if p:
              self.dropout = nn.Dropout(p)
  
      def forward(self, x):
          for module in self.children():
              x = module(x)
          return x
  