class_docstr: Apply a prior probability to the weights.
      
code: def __call__(self, shape, dtype=None):
      # set bias to -log((1 - p)/p) for foreground
      result = keras.backend.ones(shape, dtype=dtype) * -math.log(
          (1 - self.probability) / self.probability
      )
  
      return result
  
imports: from tensorflow import keras
  import math
label_desc: Use image node_id 3 for calling the PriorProbability obj's underlying member method code with example usage: obj.__call__(shape, dtype) and returns: result