class_docstr: Wrapper to pass predefined `outer_rank` to underlying projection net.
class_docstr_tok: ["Wrapper","to","pass","predefined","`","outer_rank","`","to","underlying","projection","net","."]
code: def call(self, inputs, network_state=(), **kwargs):
      kwargs["outer_rank"] = self.predefined_outer_rank
      if "step_type" in kwargs:
          del kwargs["step_type"]
      return super(_TanhNormalProjectionNetworkWrapper, self).call(
          inputs, **kwargs
      )
  
imports: import functools
  import os
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import reverb
  import tensorflow
  from tf_agents.agents.sac import sac_agent
  from tf_agents.agents.sac import tanh_normal_projection_network
  from tf_agents.environments import suite_mujoco
  from tf_agents.keras_layers import inner_reshape
  from tf_agents.metrics import py_metrics
  from tf_agents.networks import nest_map
  from tf_agents.networks import sequential
  from tf_agents.policies import greedy_policy
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.policies import random_py_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.replay_buffers import reverb_utils
  from tf_agents.train import actor
  from tf_agents.train import learner
  from tf_agents.train import triggers
  from tf_agents.train.utils import spec_utils
  from tf_agents.train.utils import strategy_utils
  from tf_agents.train.utils import train_utils
label_desc: Use image node_id 2 for calling the _TanhNormalProjectionNetworkWrapper obj's underlying member method code with example usage: obj.call(inputs, network_state) and returns: super