code: def __init__(
      self, dict_path, num_classes=512, gating=True, space_to_depth=True
  ):
      super(S3D, self).__init__()
      self.num_classes = num_classes
      self.gating = gating
      self.space_to_depth = space_to_depth
      if space_to_depth:
          self.conv1 = STConv3D(
              24,
              64,
              [2, 4, 4],
              stride=1,
              padding=(1, 2, 2),
              separable=False,
          )
      else:
          self.conv1 = STConv3D(
              3,
              64,
              [3, 7, 7],
              stride=2,
              padding=(1, 3, 3),
              separable=False,
          )
      self.conv_2b = STConv3D(64, 64, [1, 1, 1], separable=False)
      self.conv_2c = STConv3D(
          64, 192, [3, 3, 3], padding=1, separable=True
      )
      self.gating = SelfGating(192)
      self.maxpool_2a = MaxPool3dTFPadding(
          kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME"
      )
      self.maxpool_3a = MaxPool3dTFPadding(
          kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME"
      )
      self.mixed_3b = InceptionBlock(192, 64, 96, 128, 16, 32, 32)
      self.mixed_3c = InceptionBlock(
          self.mixed_3b.output_dim, 128, 128, 192, 32, 96, 64
      )
      self.maxpool_4a = MaxPool3dTFPadding(
          kernel_size=(3, 3, 3), stride=(2, 2, 2), padding="SAME"
      )
      self.mixed_4b = InceptionBlock(
          self.mixed_3c.output_dim, 192, 96, 208, 16, 48, 64
      )
      self.mixed_4c = InceptionBlock(
          self.mixed_4b.output_dim, 160, 112, 224, 24, 64, 64
      )
      self.mixed_4d = InceptionBlock(
          self.mixed_4c.output_dim, 128, 128, 256, 24, 64, 64
      )
      self.mixed_4e = InceptionBlock(
          self.mixed_4d.output_dim, 112, 144, 288, 32, 64, 64
      )
      self.mixed_4f = InceptionBlock(
          self.mixed_4e.output_dim, 256, 160, 320, 32, 128, 128
      )
      self.maxpool_5a = self.maxPool3d_5a_2x2 = MaxPool3dTFPadding(
          kernel_size=(2, 2, 2), stride=(2, 2, 2), padding="SAME"
      )
      self.mixed_5b = InceptionBlock(
          self.mixed_4f.output_dim, 256, 160, 320, 32, 128, 128
      )
      self.mixed_5c = InceptionBlock(
          self.mixed_5b.output_dim, 384, 192, 384, 48, 128, 128
      )
      self.fc = nn.Linear(self.mixed_5c.output_dim, num_classes)
      self.text_module = Sentence_Embedding(
          num_classes, token_to_word_path=dict_path
      )
  
imports: import torch
  import torch.nn.functional
  import torch.nn
  import os
  import numpy
  import re
label_desc: Use image node_id 1 to create a new S3D object from inherited base classes: nn with example: obj = S3D(dict_path, num_classes, gating, space_to_depth)