class_docstr: Custom keras layer to allow epsilon to be learned during training process.
class_docstr_tok: ["Custom","keras","layer","to","allow","epsilon","to","be","learned","during","training","process","."]
docstr: Creates a trainable weight variable for this layer.
docstr_tok: ["Creates","a","trainable","weight","variable","for","this","layer","."]
code: def build(self, input_shape):
      """
      Creates a trainable weight variable for this layer.
      """
      self.epsilon = self.add_weight(
          name="epsilon",
          shape=[1, 1],
          initializer="RandomNormal",
          trainable=True,
      )
      super(EpsilonLayer, self).build(input_shape)
  
file_path: causalml/causalml/inference/tf/utils.py
imports: import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras.layers import Layer
  from tensorflow.keras.metrics import binary_accuracy
label_desc: Use image node_id 2 for calling the EpsilonLayer obj's underlying member method code with example usage: obj.build(input_shape) without return types