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.
docstr_tok: ["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 model_utils.initialise_from_train_state(
          self.config,
          train_state,
          restored_train_state,
          restored_model_cfg,
          restore_output_proj,
      )
  
imports: import functools
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple
  from absl import logging
  import flax.linen
  from flax.linen.linear import default_kernel_init
  from immutabledict import immutabledict
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  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 attention_layers
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.baselines import vit
  from scenic.projects.mbt import model_utils
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: model_utils