code: def testScalarConditional(self):
      dist = pixel_cnn.PixelCNN(
          image_shape=self.image_shape,
          conditional_shape=(),
          num_resnet=2,
          num_hierarchies=2,
          num_filters=3,
          num_logistic_mix=2,
          high=self.high,
          low=self.low,
      )
  
      self.evaluate([v.initializer for v in dist.network.weights])
      self.evaluate(
          dist.log_prob(
              dist.sample(conditional_input=1.0), conditional_input=0.0
          )
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import pixel_cnn
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 5 for calling the ConditionalPixelCnnTest obj's underlying member method code with example usage: obj.testScalarConditional() without return types