class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
docstr: Dummy inputs to build the network.
  
  Returns:
      `Dict[str, tf.Tensor]`: The dummy inputs.
docstr_tok: ["Dummy","inputs","to","build","the","network",".","Returns",":","`","Dict","[","str",",","tf.Tensor","]","`",":","The","dummy","inputs","."]
code: def dummy_inputs(self) -> Dict[str, tf.Tensor]:
      """
      Dummy inputs to build the network.
  
      Returns:
          `Dict[str, tf.Tensor]`: The dummy inputs.
      """
      VISION_DUMMY_INPUTS = tf.random.uniform(
          shape=(3, self.config.num_channels, 224, 224),
          dtype=tf.float32,
      )
      return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
  
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 for calling the TFRegNetPreTrainedModel obj's underlying member method code with example usage: obj.dummy_inputs() and returns: dict