code: def make_bayesian_kernel_bias(
      self,
      kernel_shape,
      bias_shape,
      kernel_initializer=None,
      bias_initializer=None,
      kernel_batch_ndims=0,
      bias_batch_ndims=0,
      dtype=tf.float32,
  ):
      kernel_dist = independent.Independent(
          normal.Normal(
              tf.zeros(kernel_shape, dtype=dtype),
              tf.ones(kernel_shape, dtype=dtype),
          ),
          reinterpreted_batch_ndims=tf.size(kernel_shape)
          - kernel_batch_ndims,
      )
  
      bias_dist = independent.Independent(
          normal.Normal(
              tf.zeros(bias_shape, dtype=dtype),
              tf.ones(bias_shape, dtype=dtype),
          ),
          reinterpreted_batch_ndims=tf.size(bias_shape)
          - bias_batch_ndims,
      )
  
      wk = kernel_dist.sample(self.num_samples)
      wb = bias_dist.sample(self.num_samples)
  
      return wk, wb
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softmax_centered
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental import nn
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 1 for calling the AffineTest obj's underlying member method code with example usage: obj.make_bayesian_kernel_bias(kernel_shape, bias_shape, kernel_initializer, bias_initializer, kernel_batch_ndims, bias_batch_ndims, dtype) and returns: wk,  wb