class_docstr: Implemets the ResNet model for multi-label classification.
class_docstr_tok: ["Implemets","the","ResNet","model","for","multi-label","classification","."]
code: def build_flax_model(self) -> nn.Module:
      model_dtype = getattr(
          jnp, self.config.get("model_dtype_str", "float32")
      )
      return ResNet(
          num_outputs=self.dataset_meta_data["num_classes"],
          num_filters=self.config.num_filters,
          num_layers=self.config.num_layers,
          dtype=model_dtype,
      )
  
imports: import functools
  from typing import Tuple, Callable, Any, Optional, Union, Dict
  from absl import logging
  import flax
  import flax.linen
  from jax.nn import initializers
  import jax.numpy
  import ml_collections
  from scenic.common_lib import debug_utils
  from scenic.model_lib.base_models.classification_model import ClassificationModel
  from scenic.model_lib.base_models.multilabel_classification_model import MultiLabelClassificationModel
  from scenic.model_lib.layers import nn_layers
label_desc: Use image node_id 1 for calling the ResNetMultiLabelClassificationModel obj's underlying member method code with example usage: obj.build_flax_model() and returns: ResNet