class_docstr: Variant A of the two Reduction layers described in
  https://arxiv.org/abs/1710.02238. All variants use multiple convolutional
  blocks with varying kernel sizes and number of filters, to reduce the spatial
  extent of the image and reduce computational complexity for downstream layers.
docstr: Parameters
  ----------
  num_filters: int,
      Number of convolutional filters
docstr_tok: ["Parameters","--","--","--","--","--","num_filters",":","int",",","Number","of","convolutional","filters"]
code: def __init__(self, num_filters, **kwargs):
      """
      Parameters
      ----------
      num_filters: int,
          Number of convolutional filters
      """
      self.num_filters = num_filters
      self._build_layer_components()
      super(ReductionA, self).__init__(**kwargs)
  
imports: import tensorflow
  from tensorflow.keras.layers import Conv2D, Concatenate, ReLU, Add, MaxPool2D
label_desc: Use image node_id 1 to create a new ReductionA object from inherited base classes: tf.keras.layers with example: obj = ReductionA(num_filters)