class_docstr: Universal Vision Transformer model for multi-label classification task.
code: def build_flax_model(self) -> nn.Module:
      model_dtype = getattr(
          jnp, self.config.get("model_dtype_str", "float32")
      )
      return UViT(
          num_classes=self.dataset_meta_data["num_classes"],
          mlp_dim=self.config.model.mlp_dim,
          num_layers=self.config.model.num_layers,
          num_heads=self.config.model.num_heads,
          patches=self.config.model.patches,
          ac_config=self.config.model.get("ac_config"),
          hidden_size=self.config.model.hidden_size,
          classifier=self.config.model.classifier,
          dropout_rate=self.config.model.get("dropout_rate", 0.1),
          attention_dropout_rate=self.config.model.get(
              "attention_dropout_rate", 0.1
          ),
          stochastic_depth=self.config.model.get(
              "stochastic_depth", 0.0
          ),
          parameter_sharing=self.config.model.get(
              "parameter_sharing", True
          ),
          dtype=model_dtype,
      )
  
imports: from typing import Any, Optional
  import flax.linen
  from flax.training import common_utils
  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 model_utils
  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
  from scenic.projects.baselines import vit
  from scenic.projects.baselines.universal_transformer import layers
label_desc: Use image node_id 1 for calling the UViTMultiLabelClassificationModel obj's underlying member method code with example usage: obj.build_flax_model() and returns: UViT