code: def __init__(self, config: RegNetConfig, *inputs, **kwargs):
      super().__init__(config, *inputs, **kwargs)
      self.num_labels = config.num_labels
      self.regnet = TFRegNetMainLayer(config, name="regnet")
      # classification head
      self.classifier = [
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(config.num_labels, name="classifier.1")
          if config.num_labels > 0
          else tf.identity,
      ]
  
imports: from typing import Dict, Optional, Tuple, Union
  import tensorflow
  from ...activations_tf import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 1 to create a new TFRegNetForImageClassification object from inherited base classes: TFRegNetPreTrainedModel, TFSequenceClassificationLoss with example: obj = TFRegNetForImageClassification(config)