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: Invoked when __call__ method of the layer is used.
docstr_tok: ["Invoked","when","__call__","method","of","the","layer","is","used","."]
code: def call(self, inputs):
      """Invoked when __call__ method of the layer is used."""
      maxpool1 = self.max_pool1(inputs)
      conv1 = inputs
      for layer in self.conv_block1:
          conv1 = layer(conv1)
  
      conv2 = inputs
      for layer in self.conv_block2:
          conv2 = layer(conv2)
  
      output = self.concat_layer([maxpool1, conv1, conv2])
      output = self.activation_layer(output)
      return output
  
imports: import tensorflow
  from tensorflow.keras.layers import Conv2D, Concatenate, ReLU, Add, MaxPool2D
label_desc: Use image node_id 3 for calling the ReductionA obj's underlying member method code with example usage: obj.call(inputs) and returns: output