class_docstr: Stem Layer as defined in https://arxiv.org/abs/1710.02238. The structure is
  significantly altered from the original Inception-ResNet architecture,
  (https://arxiv.org/abs/1602.07261) but the idea behind this layer is to
  downsample the image as a preprocessing step for the Inception-ResNet layers,
  and reduce computational complexity.
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(Stem, 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 Stem object from inherited base classes: tf.keras.layers with example: obj = Stem(num_filters)