code: def __init__(
      self,
      mode,
      inputDim=256,
      hiddenDim=512,
      nClasses=500,
      frameLen=29,
      every_frame=True,
  ):
      super(Lipreading, self).__init__()
      self.mode = mode
      self.inputDim = inputDim
      self.hiddenDim = hiddenDim
      self.nClasses = nClasses
      self.frameLen = frameLen
      self.every_frame = every_frame
      self.nLayers = 2
      # frontend3D
      self.frontend3D = nn.Sequential(
          nn.Conv3d(
              1,
              64,
              kernel_size=(5, 7, 7),
              stride=(1, 2, 2),
              padding=(2, 3, 3),
              bias=False,
          ),
          nn.BatchNorm3d(64),
          nn.ReLU(True),
          nn.MaxPool3d(
              kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)
          ),
      )
      # resnet
      # self.resnet34 = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=self.inputDim)
      self.resnet18 = ResNet(
          BasicBlock, [2, 2, 2, 2], num_classes=self.inputDim
      )
      # backend_conv
      self.backend_conv1 = nn.Sequential(
          nn.Conv1d(
              self.inputDim, 2 * self.inputDim, 5, 2, 0, bias=False
          ),
          nn.BatchNorm1d(2 * self.inputDim),
          nn.ReLU(True),
          nn.MaxPool1d(2, 2),
          nn.Conv1d(
              2 * self.inputDim, 4 * self.inputDim, 5, 2, 0, bias=False
          ),
          nn.BatchNorm1d(4 * self.inputDim),
          nn.ReLU(True),
      )
      self.backend_conv2 = nn.Sequential(
          nn.Linear(4 * self.inputDim, self.inputDim),
          nn.BatchNorm1d(self.inputDim),
          nn.ReLU(True),
          nn.Linear(self.inputDim, self.nClasses),
      )
      # backend_gru
      self.gru = GRU(
          self.inputDim,
          self.hiddenDim,
          self.nLayers,
          self.nClasses,
          self.every_frame,
      )
      # initialize
      self._initialize_weights()
  
imports: import math
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Variable
label_desc: Use image node_id 1 to create a new Lipreading object from inherited base classes: nn with example: obj = Lipreading(mode, inputDim, hiddenDim, nClasses, frameLen, every_frame)