code: def i3d_joint(
      rgb_pt, flow_pt, num_classes, pretrained=False, progress=True
  ):
      """Get I3D models for different inputs.
  
      Args:
          rgb_pt (string, optional): the name of pre-trained model for RGB input.
          flow_pt (string, optional): the name of pre-trained model for flow input.
          num_classes (int): the class number of dataset.
          pretrained (bool): choose if pretrained parameters are used. (Default: False)
          progress (bool, optional): whether or not to display a progress bar to stderr. (Default: True)
  
      Returns:
          models (dictionary): A dictionary contains RGB and flow models.
      """
      i3d_rgb = i3d_flow = None
      if rgb_pt is not None:
          i3d_rgb = i3d(
              name=rgb_pt,
              num_channels=3,
              num_classes=num_classes,
              pretrained=pretrained,
              progress=progress,
          )
      if flow_pt is not None:
          i3d_flow = i3d(
              name=flow_pt,
              num_channels=2,
              num_classes=num_classes,
              pretrained=pretrained,
              progress=progress,
          )
      models = {"rgb": i3d_rgb, "flow": i3d_flow}
      return models
  
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 2 for calling a global function with example usage: i3d_joint(rgb_pt, flow_pt, num_classes, pretrained, progress) and returns: models