class_docstr: Concatenates context features and example features in a listwise manner.
  
  Given dicts of `context features`, `example features`, this layer expands
  list_size times for the `context_features` and concatenates them with
  example features along the `list_size` axis. The output is a 3-d tensor with
  shape [batch_size, list_size, sum(feature_dims)], where sum(feature_dims) is
  the sum of all example feature dimensions and the context feature dimension.
  
  Example usage:
  
  ConcatFeatures with circular padding.
  
  ```python
  # Batch size = 2, list_size = 2.
  context_features = {
      'context_feature_1': [[1.], [2.]]
  }
  example_features = {
        'example_feature_1':
            [[[1., 0.], [0., 1.]], [[0., 1.], [1., 0.]]]
  }
  mask = [[True, False], [True, True]]
  concat_tensor = ConcatFeatures()(inputs=(context_features,
      example_features, mask))
  concat_tensor.numpy()
  # Returns: [[[1., 1., 0.], [1., 1., 0.]], [[2., 0., 1.], [2., 1., 0.]]])
  ```
  
  ConcatFeatures without circular padding.
  
  ```python
  concat_tensor = ConcatFeatures(circular_padding=False)(
     inputs=(context_features, example_features, mask))
  concat_tensor.numpy()
  # Returns: [[[1., 1., 0.], [1., 0., 1.]], [[2., 0., 1.], [2., 1., 0.]]]
  ```
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 ConcatFeatures obj's underlying member method code with example usage: obj.get_config() and returns: config