class_docstr: Video Transformer model for multi-class classification.
class_docstr_tok: ["Video","Transformer","model","for","multi-class","classification","."]
docstr: Updates the train_state with data from restored_train_state.
code: def init_from_train_state(
      self,
      train_state: Any,
      restored_train_state: Any,
      restored_model_cfg: ml_collections.ConfigDict,
      restore_output_proj: bool = False,
  ) -> Any:
      """Updates the train_state with data from restored_train_state."""
      return initialise_from_train_state(
          self.config,
          train_state,
          restored_train_state,
          restored_model_cfg,
          restore_output_proj,
      )
  
imports: import functools
  import re
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple
  from absl import logging
  import flax
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.common_lib import debug_utils
  from scenic.model_lib.base_models import base_model
  from scenic.model_lib.base_models import classification_model
  from scenic.model_lib.base_models import model_utils
  from scenic.model_lib.base_models.classification_model import ClassificationModel
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.av_mae import model_utils
  from scenic.projects.baselines import vit
  from scenic.projects.mbt import model
  from scenic.projects.vivit import model_utils
  import scipy
label_desc: Use image node_id 2 for calling the MBTMultilabelClassificationModel obj's underlying member method code with example usage: obj.init_from_train_state(train_state, restored_train_state, restored_model_cfg, restore_output_proj) and returns: initialise_from_train_state