code: def __init__(self, config: ResNetConfig, **kwargs) -> None:
      super().__init__(config, **kwargs)
      self.num_labels = config.num_labels
      self.resnet = TFResNetMainLayer(config, name="resnet")
      # classification head
      self.classifier_layer = (
          tf.keras.layers.Dense(config.num_labels, name="classifier.1")
          if config.num_labels > 0
          else tf.keras.layers.Activation("linear", name="classifier.1")
      )
      self.config = config
  
imports: from typing import Optional, Tuple, Union
  import tensorflow
  from ...activations_tf import ACT2FN
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFImageClassifierOutputWithNoAttention
  from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_resnet import ResNetConfig
label_desc: Use image node_id 1 to create a new TFResNetForImageClassification object from inherited base classes: TFResNetPreTrainedModel, TFSequenceClassificationLoss with example: obj = TFResNetForImageClassification(config)