class_docstr: Vision Transformer model for LOCA training.
code: def default_flax_model_config(self) -> ml_collections.ConfigDict:
      return ml_collections.ConfigDict(
          {
              "model": dict(
                  num_heads=2,
                  num_layers=1,
                  mlp_dim=32,
                  dropout_rate=0.0,
                  attention_dropout_rate=0.0,
                  hidden_size=16,
                  head_hidden_dim=32,
                  head_bottleneck_dim=16,
                  head_output_dim=64,
                  patches={"size": (4, 4)},
                  data_dtype_str="float32",
              )
          }
      )
  
imports: import copy
  import functools
  from typing import Any, Optional, Tuple
  import flax.linen
  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.layers import attention_layers
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.baselines import vit
label_desc: Use image node_id 2 for calling the ViTLOCAModel obj's underlying member method code with example usage: obj.default_flax_model_config() and returns: ml_collections