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):
      """
      Dummy inputs to build the network.
  
      Returns:
          `Dict[str, tf.Tensor]`: The dummy inputs.
      """
      input_ids = tf.constant(DUMMY_INPUTS, dtype=tf.int32)
      batch_size, seq_len = input_ids.shape
  
      VISION_DUMMY_INPUTS = tf.random.uniform(
          shape=(
              batch_size,
              self.config.vision_config.num_channels,
              self.config.vision_config.image_size,
              self.config.vision_config.image_size,
          ),
          dtype=tf.float32,
      )
      pixel_values = tf.constant(VISION_DUMMY_INPUTS)
      dummy = {"pixel_values": pixel_values, "input_ids": input_ids}
      return dummy
  
imports: from __future__ import annotations
  import re
  from typing import Optional, Tuple, Union
  import tensorflow
  from tensorflow.keras.layers import Dense
  from ...configuration_utils import PretrainedConfig
  from ...modeling_tf_utils import TFPreTrainedModel, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import DUMMY_INPUTS, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ..auto.configuration_auto import AutoConfig
  from ..auto.modeling_tf_auto import TFAutoModel
  from ..clip.modeling_tf_clip import CLIPVisionConfig, TFCLIPOutput, TFCLIPVisionModel
  from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
label_desc: Use image node_id 8 for calling the TFVisionTextDualEncoderModel obj's underlying member method code with example usage: obj.dummy_inputs() and returns: dummy