class_docstr: Univariate scorer using DNN.
  
  Example usage:
  
  ```python
  scorer=DNNScorer(hidden_layer_dims=[16])
  ```
docstr: See `UnivariateScorer`.
docstr_tok: ["See","`","UnivariateScorer","`","."]
code: def _score_flattened(
      self,
      context_features: TensorDict,
      example_features: TensorDict,
  ) -> tf.Tensor:
      """See `UnivariateScorer`."""
      context_input_layer = [
          tf.keras.layers.Flatten()(context_features[name])
          for name in sorted(context_features)
      ]
      example_input_layer = [
          tf.keras.layers.Flatten()(example_features[name])
          for name in sorted(example_features)
      ]
      input_layer = tf.concat(
          context_input_layer + example_input_layer, 1
      )
      flattened_logits = layers.create_tower(**self._dnn_kwargs)(
          input_layer
      )
  
      return flattened_logits
  
imports: import abc
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  from absl import logging
  import tensorflow
  from tensorflow_ranking.python.keras import feature
  from tensorflow_ranking.python.keras import layers
label_desc: Use image node_id 2 for calling the DNNScorer obj's underlying member method code with example usage: obj._score_flattened(context_features, example_features) and returns: flattened_logits