code: def prior_trainable(kernel_size, bias_size=0, dtype=None):
      n = kernel_size + bias_size
      return tf_keras.Sequential(
          [
              variable_input.VariableLayer(n, dtype=dtype),
              distribution_layer.DistributionLambda(
                  lambda t: independent.Independent(
                      normal.Normal(
                          loc=t, scale=1
                      ),  # pylint: disable=g-long-lambda
                      reinterpreted_batch_ndims=1,
                  )
              ),
          ]
      )
  
file_path: probability/tensorflow_probability/python/layers/dense_variational_v2_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.layers import dense_variational_v2
  from tensorflow_probability.python.layers import distribution_layer
  from tensorflow_probability.python.layers import variable_input
label_desc: Use image node_id 3 for calling a global function with example usage: prior_trainable(kernel_size, bias_size, dtype) and returns: tf_keras