class_docstr: ViViT model for Multi-Modality Masked AutoEncoder.
  
  
  Attributes:
    mlp_dim: Dimension of the mlp on top of attention block.
    num_classes_dict: Dictionary with the number of output classes.
    num_heads: Number of self-attention heads.
    num_layers: Number of layers.
    patches: Configuration of the patches extracted in the stem of the model.
    hidden_size: Size of the hidden state of the output of model's stem.
    token_mask_probability_dict: Probability of dropping out the input tokens
    during training.
    masking_strategy: Masking strategy used to mask the tokens.
    temporal_encoding_config: ConfigDict which defines the type of input
      encoding when tokenising the video.
    decoder_config: ConfigDict which define the decoder.
    attention_config: ConfigDict which defines the type of spatio-temporal
      attention applied in the model.
    dropout_rate: Dropout rate.
    attention_dropout_rate: Dropout for attention heads.
    stochastic_droplayer_rate: Probability of dropping a layer. Linearly
      increases from 0 to the provided value..
    classifier: type of the classifier layer. Options are 'gap', 'gmp', 'gsp',
      'token'.
    encoder_strategy: Specify how to combine the modalities in the encoder.
      Choose from: separate_encoders, concat_and_encode, same_encoder.
    decoder_strategy: Specify how to combine the modalities in the decoder,
      Choose from: separate_decoders, same_decoder.
    use_inpainting: Whether or not to use the modality inpaiting strategy.
    normalise_encoder_output: If true, layer normalisation is applied to the
      output of the transformer encoder.
    use_modality_tokens: If True, modality learnable tokens are added.
    fusion_layers: When the encoder strategy is 'encode_and_concat',
      this specify how many layers to use for concatenation.
    dtype: JAX data type for activations.
docstr: "Remove the cls token.
docstr_tok: ["``","Remove","the","cls","token","."]
code: def remove_cls_token(
      self, x_unmasked_proj_dict: ArrayDict
  ) -> Tuple[ArrayDict, ArrayDict]:
      """ "Remove the cls token."""
      cls_encoded_dict = {}
      if self.classifier == "token":
          raise NotImplementedError(
              "Token classifier is not implemented yet!"
          )
      return cls_encoded_dict, x_unmasked_proj_dict
  
imports: import functools
  from typing import Any, Dict, Optional, Tuple
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib.base_models import base_model
  from scenic.model_lib.base_models import model_utils
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.av_mae import base_model
  from scenic.projects.av_mae import model_utils
  from scenic.projects.mbt import model
  from scenic.projects.vivit import model
  from scenic.projects.vivit import model_utils
label_desc: Use image node_id 7 for calling the ViViTMultiMaskedAutoencoder obj's underlying member method code with example usage: obj.remove_cls_token(x_unmasked_proj_dict) and returns: cls_encoded_dict,  x_unmasked_proj_dict