code: def __init__(self, frames=3, flownet_path="", fp16=False):
      super(VSRNet, self).__init__()
  
      self.frames = frames
      self.fp16 = fp16
      self.mi = int(floor(self.frames / 2))
  
      self.pooling = nn.AvgPool2d(4, ceil_mode=False)
      self.upsample = nn.Upsample(scale_factor=4, mode="bilinear")
  
      if fp16:
          # from FlowNetSD16 import FlowNetSD
          from FlowNetSD import FlowNetSD
      else:
          from FlowNetSD import FlowNetSD
  
      FlowNetSD_network = FlowNetSD(args=[], batchNorm=False)
      try:
          FlowNetSD_weights = torch.load(flownet_path)["state_dict"]
      except:
          raise IOError(
              "FlowNet weights could not be loaded from %s"
              % flownet_path
          )
      FlowNetSD_network.load_state_dict(FlowNetSD_weights)
      self.FlowNetSD_network = FlowNetSD_network
  
      self.train_grid = None
      self.val_grid = None
  
      self.batchNorm = True
      self.conv1 = conv(self.batchNorm, 1, 64, kernel_size=9)
      self.conv2 = conv(
          self.batchNorm, 64 * self.frames, 32, kernel_size=5
      )
      self.conv3 = nn.Conv2d(
          32, 1, kernel_size=5, stride=1, padding=2, bias=True
      )
      self.conv3.weight = torch.nn.init.normal(
          self.conv3.weight, 0, 0.1
      )
  
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 1 to create a new VSRNet object from inherited base classes: nn with example: obj = VSRNet(frames, flownet_path, fp16)