class_docstr: Vision Transformer model for multi-label classification task.
code: def default_flax_model_config(self) -> ml_collections.ConfigDict:
      return ml_collections.ConfigDict(
          {
              "model": dict(
                  num_heads=2,
                  num_layers=1,
                  representation_size=16,
                  mlp_dim=32,
                  dropout_rate=0.0,
                  attention_dropout_rate=0.0,
                  hidden_size=16,
                  patches={"grid": (4, 4)},
                  classifier="gap",
                  data_dtype_str="float32",
              )
          }
      )
  
imports: from typing import Any, Callable, Optional, Sequence
  from absl import logging
  import flax
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.base_models.multilabel_classification_model import MultiLabelClassificationModel
  from scenic.model_lib.layers import attention_layers
  from scenic.model_lib.layers import nn_layers
  import scipy
label_desc: Use image node_id 2 for calling the ViTMultiLabelClassificationModel obj's underlying member method code with example usage: obj.default_flax_model_config() and returns: ml_collections