code: def __init__(self, config: DPRConfig, **kwargs):
      super().__init__(**kwargs)
      self.config = config
      self.encoder = TFDPREncoderLayer(config, name="encoder")
  
      self.qa_outputs = tf.keras.layers.Dense(
          2,
          kernel_initializer=get_initializer(config.initializer_range),
          name="qa_outputs",
      )
      self.qa_classifier = tf.keras.layers.Dense(
          1,
          kernel_initializer=get_initializer(config.initializer_range),
          name="qa_classifier",
      )
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import tensorflow
  from ...modeling_tf_outputs import TFBaseModelOutputWithPooling
  from ...modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list, unpack_inputs
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ..bert.modeling_tf_bert import TFBertMainLayer
  from .configuration_dpr import DPRConfig
label_desc: Use image node_id 1 to create a new TFDPRSpanPredictorLayer object from inherited base classes: tf.keras.layers with example: obj = TFDPRSpanPredictorLayer(config)