class_docstr: Layer to flatten the example list.
  
  This layer flattens the batch_size dimension and the list_size dimension for
  the `example_features` and expands list_size times for the `context_features`.
  
  Example usage:
  
  ```python
  context_features = {'context_feature_1': [[1], [0]]}
  example_features = {'example_feature_1':
                      [[[1], [0], [-1]], [[0], [1], [0]]]}
  mask = [[True, True, False], [True, False, False]]
  flattened_context_features, flattened_example_features = FlattenList()(
      inputs=(context_features, example_features, mask))
  ```
  That is, there are two valid examples in the first query and one
  valid example in the second query. Then
  
  ```python
  flattened_context_features = {'context_feature_1':
                                [[1], [1], [1], [0], [0], [0]]}
  flattened_example_features = {'example_feature_1':
                                [[1], [0], [1], [0], [0], [0]]}
  ```
  `context_feature_1` is repeated by list_size=3 times. `example_feature_1` is
  flattened and padded with the invalid terms replaced by valid terms in each
  query in a circular way.
code: def get_config(self):
      config = super().get_config()
      config.update(
          {
              "circular_padding": self._circular_padding,
          }
      )
      return config
  
imports: import math
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import tensorflow
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 3 for calling the FlattenList obj's underlying member method code with example usage: obj.get_config() and returns: config