code: def mvit_base_16x4(
      pretrained: bool = False,
      progress: bool = True,
      **kwargs: Any,
  ) -> nn.Module:
      """
      Multiscale Vision Transformers model architecture [1] trained with an 16x4
      setting on the Kinetics400 dataset. Model with pretrained weights has top1
      accuracy of 78.9%.
  
      [1] Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra
      Malik, Christoph Feichtenhofer, "Multiscale Vision Transformers"
      https://arxiv.org/pdf/2104.11227.pdf
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on Kinetics400 dataset.
          progress (bool): If True, displays a progress bar of the download to stderr.
          kwargs: Use these to modify any of the other model settings. All the
              options are defined in create_multiscale_vision_transformers.
  
      NOTE: to use the pretrained model, do not modify the model configuration
      via the kwargs. Only modify settings via kwargs to initialize a new model
      without pretrained weights.
      """
  
      return hub_model_builder(
          model_builder_func=create_multiscale_vision_transformers,
          pretrained=pretrained,
          progress=progress,
          checkpoint_path=checkpoint_paths["mvit_base_16x4"],
          default_config=mvit_video_base_config,
          **kwargs,
      )
  
imports: from typing import Any
  import torch.nn
  from pytorchvideo.models.vision_transformers import create_multiscale_vision_transformers
  from .utils import hub_model_builder, MODEL_ZOO_ROOT_DIR
label_desc: Use image node_id 1 for calling a global function with example usage: mvit_base_16x4(pretrained, progress) and returns: hub_model_builder