class_docstr: A transition used for up-sampling.
  
  Up-sample the input by interpolation then refines the feature by
  a convolution layer.
  
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
      scale_factor (int): Up-sampling factor. Default: 2.
      mode (int): Interpolation mode. Default: nearest.
      align_corners (bool): Whether align corners when interpolation.
          Default: None.
      kernel_size (int): Kernel size for the conv. Default: 3.
code: def forward(self, x):
      x = F.interpolate(
          x,
          scale_factor=self.scale_factor,
          mode=self.mode,
          align_corners=self.align_corners,
      )
      x = self.conv(x)
      return x
  
imports: import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule
  from mmdet.registry import MODELS
label_desc: Use image node_id 2 for calling the UpInterpolationConv obj's underlying member method code with example usage: obj.forward(x) and returns: x