class_docstr: Hybrid Vision Transformer model for multi-label classification task.
class_docstr_tok: ["Hybrid","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={"size": (4, 4)},
                  resnet={"num_layers": (1,), "width_factor": 1},
                  classifier="gap",
                  data_dtype_str="float32",
              )
          }
      )
  
file_path: scenic/scenic/projects/baselines/hybrid_vit.py
imports: from typing import Any, Optional
  import flax.linen
  import jax.numpy
  import ml_collections
  from scenic.model_lib.base_models.multilabel_classification_model import MultiLabelClassificationModel
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.baselines import bit_resnet
  from scenic.projects.baselines import vit
label_desc: Use image node_id 2 for calling the HybridViTMultiLabelClassificationModel obj's underlying member method code with example usage: obj.default_flax_model_config() and returns: ml_collections