class_docstr: MTV model.
docstr: Encodes input tokens on a per-time basis.
docstr_tok: ["Encodes","input","tokens","on","a","per-time","basis","."]
code: def _encode_per_time(
      self,
      tokens: Sequence[jnp.ndarray],
      bottleneck: Union[jnp.ndarray, None],
      is_train: bool,
  ) -> List[jnp.ndarray]:
      """Encodes input tokens on a per-time basis."""
  
      tokens = MultiviewEncoder(
          view_configs=self.view_configs,
          cross_view_fusion=self.cross_view_fusion,
          input_token_temporal_dims=self.input_token_temporal_dims,
          dropout_rate=self.dropout_rate,
          attention_dropout_rate=self.attention_dropout_rate,
          stochastic_depth=self.stochastic_depth,
          dtype=self.dtype,
          name="MultiviewEncoder",
      )(tokens, bottleneck=bottleneck, train=is_train)
      return tokens
  
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 10 for calling the MTV obj's underlying member method code with example usage: obj._encode_per_time(tokens, bottleneck, is_train) and returns: tokens