class_docstr: Leaky Clipped Linear Unit.
  
      Args:
          low (float): Lower threshold
          high (float): Lower threshold
          alpha (float) The slope of the function below low or above high.
class_docstr_tok: ["Leaky","Clipped","Linear","Unit",".","Args",":","low","(","float",")",":","Lower","threshold","high","(","float",")",":","Lower","threshold","alpha","(","float",")","The","slope","of","the","function","below","low","or","above","high","."]
code: def __init__(
      self,
      low: float = 1.0,
      high: float = 5.0,
      alpha: float = 0.1,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.supports_masking = True
      self.gamma = K.cast_to_floatx(1 - alpha)
      self.lo = K.cast_to_floatx(low)
      self.hi = K.cast_to_floatx(high)
  
file_path: stellargraph/stellargraph/layer/link_inference.py
imports: from typing import AnyStr, Optional, List, Tuple
  import tensorflow
  from tensorflow.keras.layers import Layer, Concatenate, Dense, Lambda, Multiply, Average, Reshape, Activation
  from tensorflow.keras import backend
  import warnings
label_desc: Use image node_id 1 to create a new LeakyClippedLinear object from inherited base classes: Layer with example: obj = LeakyClippedLinear(low, high, alpha)