code: def i3d(
      name, num_channels, num_classes, pretrained=False, progress=True
  ):
      """Get InceptionI3d module w/o pretrained model."""
      model = InceptionI3d(
          in_channels=num_channels, num_classes=num_classes
      )
  
      if pretrained:
          state_dict = load_state_dict_from_url(
              model_urls[name], progress=progress
          )
  
          # delete the last layer's parameter and only load the parameters before the last due to different class number.
          # uncomment and change the output size of I3D when using the default classifier in I3D.
  
          # state_dict.pop("logits.conv3d.weight")
          # state_dict.pop("logits.conv3d.bias")
          # model.load_state_dict(state_dict, strict=False)
  
          model.load_state_dict(state_dict)
      return model
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 1 for calling a global function with example usage: i3d(name, num_channels, num_classes, pretrained, progress) and returns: model