code: def _get_single_pixel_logit_gradients(
      self, dist, logit_ind, pixel_ind
  ):
      h = self._make_fake_conditional()
  
      def g(x):
          x = (2.0 * (x - self.low) / (self.high - self.low)) - 1.0
          inputs = [x, h] if h is not None else x
          params = dist.network(inputs)
          out = self._apply_channel_conditioning(dist, x, *params)
          return out[logit_ind][pixel_ind]
  
      image_input = self._make_fake_images()
      _, grads = gradient.value_and_gradient(g, image_input)
      return self.evaluate(grads)
  
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 7 for calling the PixelCnnTest obj's underlying member method code with example usage: obj._get_single_pixel_logit_gradients(dist, logit_ind, pixel_ind) and returns: self, out