code: def _get_normal_distribution_spec(self, sample_spec):
      param_properties = tfp.distributions.Normal.parameter_properties()
      input_param_spec = {  # pylint: disable=g-complex-comprehension
          name: tensor_spec.TensorSpec(
              shape=properties.shape_fn(sample_spec.shape),
              dtype=sample_spec.dtype,
          )
          for name, properties in param_properties.items()
      }
  
      return distribution_spec.DistributionSpec(
          tfp.distributions.Normal,
          input_param_spec,
          sample_spec=sample_spec,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.ppo import ppo_policy
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import mask_splitter_network
  from tf_agents.networks import network
  from tf_agents.networks import sequential
  from tf_agents.networks import value_network
  from tf_agents.specs import distribution_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling the DummyActorDistributionNet obj's underlying member method code with example usage: obj._get_normal_distribution_spec(sample_spec) and returns: distribution_spec