class_docstr: Continuous MLP Policy Network.
  
  The policy network selects action based on the state of the environment.
  It uses neural nets to fit the function of pi(s).
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      name (str): Policy name, also the variable scope.
      hidden_sizes (list[int]): Output dimension of dense layer(s).
          For example, (32, 32) means the MLP of this policy consists of two
          hidden layers, each with 32 hidden units.
      hidden_nonlinearity (callable): Activation function for intermediate
          dense layer(s). It should return a tf.Tensor. Set it to
          None to maintain a linear activation.
      hidden_w_init (callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      output_nonlinearity (callable): Activation function for output dense
          layer. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      output_w_init (callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          tf.Tensor.
      output_b_init (callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          tf.Tensor.
      layer_normalization (bool): Bool for using layer normalization or not.
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docstr: Get regularizable weight variables under the Policy scope.
  
  Returns:
      list(tf.Variable): List of regularizable variables.
docstr_tok: ["Get","regularizable","weight","variables","under","the","Policy","scope",".","Returns",":","list","(","tf.Variable",")",":","List","of","regularizable","variables","."]
code: def get_regularizable_vars(self):
      """Get regularizable weight variables under the Policy scope.
  
      Returns:
          list(tf.Variable): List of regularizable variables.
  
      """
      trainable = self.get_trainable_vars()
      return [
          var
          for var in trainable
          if "hidden" in var.name and "kernel" in var.name
      ]
  
file_path: garage/src/garage/tf/policies/continuous_mlp_policy.py
imports: import numpy
  import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models import MLPModel
  from garage.tf.policies.policy import Policy
label_desc: Use image node_id 7 for calling the ContinuousMLPPolicy obj's underlying member method code with example usage: obj.get_regularizable_vars() and returns: unknown