code: def forward(self, inputs, iteration, writer, im_out=False):
      batchsize, channels, frames, rows, cols = inputs.size()
  
      # inputs are normalized
      y, cb, cr = rgb2ycbcr(inputs)
      y /= 255
      target = y[:, :, self.mi, :, :]
  
      if writer is not None and im_out:
          out_im = inputs[
              0, :, self.mi, :, :
          ]  # / 255.0 will we need this?
          tensorboard_image("target", out_im, iteration, writer)
          out_im = self.pooling(out_im)
          tensorboard_image("downsampled", out_im, iteration, writer)
          out_im = self.upsample(out_im.unsqueeze(0)).squeeze(0)
          tensorboard_image("upsampled", out_im, iteration, writer)
  
      # Compute per RGB channel mean across pixels for each image in input batch
      rgb_mean = (
          inputs.view((batchsize, channels) + (-1,))
          .float()
          .mean(dim=-1)
      )
      rgb_mean = rgb_mean.view(
          (batchsize, channels)
          + (
              1,
              1,
              1,
          )
      )
      if self.fp16:
          rgb_mean = rgb_mean.half()
  
      inputs = (inputs - rgb_mean) / 255
  
      if self.training:
          if self.train_grid is None:
              self.train_grid = get_grid(
                  batchsize, rows, cols, self.fp16
              )
          grid = self.train_grid
      else:
          if self.val_grid is None:
              self.val_grid = get_grid(batchsize, rows, cols, self.fp16)
          grid = self.val_grid
      grid.requires_grad = False
  
      downsampled_input = self.pooling(cb[:, :, self.mi, :, :])
      cb[:, :, self.mi, :, :] = self.upsample(downsampled_input)
      downsampled_input = self.pooling(cr[:, :, self.mi, :, :])
      cr[:, :, self.mi, :, :] = self.upsample(downsampled_input)
  
      conv1_out = []
      for fr in range(self.frames):
          downsampled_input = self.pooling(y[:, :, fr, :, :])
          y[:, :, fr, :, :] = self.upsample(downsampled_input)
  
          if fr == self.mi:
              conv1_out.append(
                  self.conv1(y[:, :, self.mi, :, :].clone())
              )
          else:
              im1 = inputs[:, :, fr, :, :]
              im2 = inputs[:, :, self.mi, :, :]
              im_pair = torch.cat((im2, im1), 1)
  
              to_warp = y[:, :, fr, :, :]
  
              flow = (
                  self.upsample(self.FlowNetSD_network(im_pair)[0]) / 16
              )
  
              flow = torch.cat(
                  [
                      flow[:, 0:1, :, :] / ((cols - 1.0) / 2.0),
                      flow[:, 1:2, :, :] / ((rows - 1.0) / 2.0),
                  ],
                  1,
              )
  
              warped = torch.nn.functional.grid_sample(
                  input=to_warp,
                  grid=(grid + flow).permute(0, 2, 3, 1),
                  mode="bilinear",
                  padding_mode="border",
              )
  
              conv1_out.append(self.conv1(warped))
  
      conv1_out = torch.cat(conv1_out, 1)
  
      conv2_out = self.conv2(conv1_out)
  
      # Loss must be computed for pixel values in [0, 255] to prevent
      # divergence in fp16
      prediction = torch.nn.functional.sigmoid(
          self.conv3(conv2_out).float()
      )
  
      loss = torch.nn.functional.mse_loss(
          prediction.float(), target.float()
      )
  
      if not self.training:
          # Following [1], remove 12 pixels around border to prevent
          # convolution edge effects affecting PSNR
          psnr_metric = psnr(
              prediction[:, :, 12:, :-12].float() * 255,
              target[:, :, 12:, :-12].float() * 255,
          )
  
      prediction = ycbcr2rgb(
          torch.cat(
              (
                  prediction * 255,
                  cb[:, :, self.mi, :, :],
                  cr[:, :, self.mi, :, :],
              ),
              1,
          )
      )
  
      if writer is not None and im_out:
          out_im = prediction[0, :, :, :]
          tensorboard_image("prediction", out_im, iteration, writer)
  
      if self.training:
          return loss
      else:
          return loss, psnr_metric
  
imports: import time
  import scipy.misc
  import numpy
  from math import floor, log
  import torch
  import torch.nn
  from torch.nn import init
  from torch.autograd import Variable
  from torch.nn.functional import upsample
  import sys
label_desc: Use image node_id 2 for calling the VSRNet obj's underlying member method code with example usage: obj.forward(inputs, iteration, writer, im_out) and returns: loss, loss,  psnr_metric