original_docstring: Upsample and add two feature maps.
  
          Args:
            x: (Variable) top feature map to be upsampled.
            y: (Variable) lateral feature map.
  
          Returns:
            (Variable) added feature map.
  
          Note in PyTorch, when input size is odd, the upsampled feature map
          with `F.upsample(..., scale_factor=2, mode='nearest')`
          maybe not equal to the lateral feature map size.
  
          e.g.
          original input size: [N,_,15,15] ->
          conv2d feature map size: [N,_,8,8] ->
          upsampled feature map size: [N,_,16,16]
  
          So we choose bilinear upsample which supports arbitrary output sizes.
          
docstring: Upsample and add two feature maps.
docstring_tokens: Upsample and add two feature maps .
code: def _upsample_add(self, x, y):
          '''Upsample and add two feature maps.
  
          Args:
            x: (Variable) top feature map to be upsampled.
            y: (Variable) lateral feature map.
  
          Returns:
            (Variable) added feature map.
  
          Note in PyTorch, when input size is odd, the upsampled feature map
          with `F.upsample(..., scale_factor=2, mode='nearest')`
          maybe not equal to the lateral feature map size.
  
          e.g.
          original input size: [N,_,15,15] ->
          conv2d feature map size: [N,_,8,8] ->
          upsampled feature map size: [N,_,16,16]
  
          So we choose bilinear upsample which supports arbitrary output sizes.
          '''
          _,_,H,W = y.size()
          return F.interpolate(x, size=(H,W), mode='bilinear') + y
code_tokens: def _upsample_add ( self , x , y ) : _ , _ , H , W = y . size ( ) return F . interpolate ( x , size = ( H , W ) , mode = 'bilinear' ) + y
short_docstring: Upsample and add two feature maps.
short_docstring_tokens: Upsample and add two feature maps .
comment: '''Upsample and add two feature maps.
  
          Args:
            x: (Variable) top feature map to be upsampled.
            y: (Variable) lateral feature map.
  
          Returns:
            (Variable) added feature map.
  
          Note in PyTorch, when input size is odd, the upsampled feature map
          with `F.upsample(..., scale_factor=2, mode='nearest')`
          maybe not equal to the lateral feature map size.
  
          e.g.
          original input size: [N,_,15,15] ->
          conv2d feature map size: [N,_,8,8] ->
          upsampled feature map size: [N,_,16,16]
  
          So we choose bilinear upsample which supports arbitrary output sizes.
          '''