code: def __init__(self, out_channel, kernel_size=3, stride=1, **kwargs):
      super(ConvBNReLU, self).__init__(**kwargs)
      self.conv = layers.Conv2D(
          filters=out_channel,
          kernel_size=kernel_size,
          strides=stride,
          padding="SAME",
          use_bias=False,
          name="Conv2d",
      )
      self.bn = layers.BatchNormalization(
          momentum=0.9, epsilon=1e-5, name="BatchNorm"
      )
      self.activation = layers.ReLU(max_value=6.0)
  
file_path: deep-learning-for-image-processing/tensorflow_classification/Test6_mobilenet/model_v2.py
imports: from tensorflow.keras import layers, Model, Sequential
label_desc: Use image node_id 1 to create a new ConvBNReLU object from inherited base classes: layers with example: obj = ConvBNReLU(out_channel, kernel_size, stride)