class_docstr: MTV model.
docstr: Prepends CLS tokens for all views.
  
  Args:
    tokens: Tokens from all views. Each one has a shape of (batch, time,
      sequence_len, channels)
  
  Returns:
    A list of tokens with CLS tokens added. Each one has a new shape of
    (batch, time, sequence_len+1, channels).
docstr_tok: ["Prepends","CLS","tokens","for","all","views",".","Args",":","tokens",":","Tokens","from","all","views",".","Each","one","has","a","shape","of","(","batch",",","time",",","sequence_len",",","channels",")","Returns",":","A","list","of","tokens","with","CLS","tokens","added",".","Each","one","has","a","new","shape","of","(","batch",",","time",",","sequence_len+1",",","channels",")","."]
code: def _add_cls_tokens_for_all_views(
      self, tokens: Sequence[jnp.ndarray]
  ) -> List[jnp.ndarray]:
      """Prepends CLS tokens for all views.
  
      Args:
        tokens: Tokens from all views. Each one has a shape of (batch, time,
          sequence_len, channels)
  
      Returns:
        A list of tokens with CLS tokens added. Each one has a new shape of
        (batch, time, sequence_len+1, channels).
      """
      outputs = []
      for idx, x in enumerate(tokens):
          outputs.append(
              self._add_cls_tokens_all_frames(x, name=f"cls_view{idx}")
          )
      return outputs
  
imports: import functools
  from typing import Any, List, Sequence, Tuple, Union
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  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.mtv import model_utils
  from scenic.projects.vivit import model
  from scenic.train_lib import train_utils
label_desc: Use image node_id 3 for calling the MTV obj's underlying member method code with example usage: obj._add_cls_tokens_for_all_views(tokens) and returns: outputs