original_docstring: 
          Creates a convolutional module followed by a ReLU
          
docstring: Creates a convolutional module followed by a ReLU
docstring_tokens: Creates a convolutional module followed by a ReLU
code: def _conv_block(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1):
          """
          Creates a convolutional module followed by a ReLU
          """
          if self.is_3D:
              return nn.Sequential(
                  nn.Conv3d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=(1, dilation, dilation)),
                  nn.ReLU(),
                  nn.BatchNorm3d(out_channels)
              )
          else:
              return nn.Sequential(
                  nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation),
                  nn.ReLU(),
                  nn.BatchNorm2d(out_channels)
              )
code_tokens: def _conv_block ( self , in_channels , out_channels , kernel_size = 3 , stride = 1 , padding = 1 , dilation = 1 ) : if self . is_3D : return nn . Sequential ( nn . Conv3d ( in_channels , out_channels , kernel_size = kernel_size , stride = stride , padding = padding , dilation = ( 1 , dilation , dilation ) ) , nn . ReLU ( ) , nn . BatchNorm3d ( out_channels ) ) else : return nn . Sequential ( nn . Conv2d ( in_channels , out_channels , kernel_size = kernel_size , stride = stride , padding = padding , dilation = dilation ) , nn . ReLU ( ) , nn . BatchNorm2d ( out_channels ) )
short_docstring: Creates a convolutional module followed by a ReLU
short_docstring_tokens: Creates a convolutional module followed by a ReLU
comment: """
          Creates a convolutional module followed by a ReLU
          """