code: def create_layers(self, state, **kwargs):
      d1 = Flatten(in_layers=state)
      d2 = Dense(
          in_layers=[d1],
          activation_fn=tf.nn.relu,
          normalizer_fn=tf.nn.l2_normalize,
          normalizer_params={"dim": 1},
          out_channels=64,
      )
      d3 = Dense(
          in_layers=[d2],
          activation_fn=tf.nn.relu,
          normalizer_fn=tf.nn.l2_normalize,
          normalizer_params={"dim": 1},
          out_channels=32,
      )
      d4 = Dense(
          in_layers=[d3],
          activation_fn=tf.nn.relu,
          normalizer_fn=tf.nn.l2_normalize,
          normalizer_params={"dim": 1},
          out_channels=16,
      )
      d4 = BatchNorm(in_layers=[d4])
      d5 = Dense(in_layers=[d4], activation_fn=None, out_channels=9)
      value = Dense(in_layers=[d4], activation_fn=None, out_channels=1)
      value = Squeeze(squeeze_dims=1, in_layers=[value])
      probs = SoftMax(in_layers=[d5])
      return {"action_prob": probs, "value": value}
  
file_path: deepchem/contrib/rl/tictactoe.py
imports: import copy
  import random
  import shutil
  import numpy
  import tensorflow
  import deepchem
  import deepchem.rl.envs.tictactoe
  from deepchem.models.tensorgraph.layers import Flatten, Dense, SoftMax, BatchNorm, Squeeze
  from deepchem.models.optimizers import Adam
label_desc: Use image node_id 1 for calling the TicTacToePolicy obj's underlying member method code with example usage: obj.create_layers(state) and returns: dict