class_docstr: Vision Transformer model for masked autoencoder pretraining.
class_docstr_tok: ["Vision","Transformer","model","for","masked","autoencoder","pretraining","."]
code: def default_flax_model_config(self) -> ml_collections.ConfigDict:
      return ml_collections.ConfigDict(
          {
              "model": dict(
                  num_heads=2,
                  num_layers=1,
                  representation_size=None,
                  mlp_dim=64,
                  dropout_rate=0.0,
                  attention_dropout_rate=0.0,
                  hidden_size=16,
                  patches={"size": (4, 4)},
                  classifier="token",
                  data_dtype_str="float32",
                  decoder_config=dict(
                      num_heads=2,
                      num_layers=1,
                      mlp_dim=32,
                      hidden_size=8,
                      dropout_rate=0,
                      attention_dropout_rate=0,
                      stochastic_depth=0,
                  ),
              ),
              "masked_feature_loss": dict(
                  target="rgb", token_mask_probability=0.75
              ),
          }
      )
  
imports: from typing import Any, Callable, Optional, Sequence
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib.base_models import classification_model
  from scenic.model_lib.base_models import multilabel_classification_model
  from scenic.model_lib.layers import attention_layers
  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.baselines import vit
label_desc: Use image node_id 2 for calling the ViTMaskedAutoencoderModel obj's underlying member method code with example usage: obj.default_flax_model_config() and returns: ml_collections