class_docstr: Gaussian GRU Policy.
  
  A policy represented by a Gaussian distribution
  which is parameterized by a Gated Recurrent Unit (GRU).
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      name (str): Model name, also the variable scope.
      hidden_dim (int): Hidden dimension for GRU cell for mean.
      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.
      recurrent_nonlinearity (Callable): Activation function for recurrent
          layers. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      recurrent_w_init (Callable): Initializer function for the weight
          of recurrent 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.
      hidden_state_init (Callable): Initializer function for the
          initial hidden state. The functino should return a tf.Tensor.
      hidden_state_init_trainable (bool): Bool for whether the initial
          hidden state is trainable.
      learn_std (bool): Is std trainable.
      std_share_network (bool): Boolean for whether mean and std share
          the same network.
      init_std (float): Initial value for std.
      layer_normalization (bool): Bool for using layer normalization or not.
      state_include_action (bool): Whether the state includes action.
          If True, input dimension will be
          (observation dimension + action dimension).
class_docstr_tok: ["Gaussian","GRU","Policy",".","A","policy","represented","by","a","Gaussian","distribution","which","is","parameterized","by","a","Gated","Recurrent","Unit","(","GRU",")",".","Args",":","env_spec","(","garage.envs.env_spec.EnvSpec",")",":","Environment","specification",".","name","(","str",")",":","Model","name",",","also","the","variable","scope",".","hidden_dim","(","int",")",":","Hidden","dimension","for","GRU","cell","for","mean",".","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",".","recurrent_nonlinearity","(","Callable",")",":","Activation","function","for","recurrent","layers",".","It","should","return","a","tf.Tensor",".","Set","it","to","None","to","maintain","a","linear","activation",".","recurrent_w_init","(","Callable",")",":","Initializer","function","for","the","weight","of","recurrent","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",".","hidden_state_init","(","Callable",")",":","Initializer","function","for","the","initial","hidden","state",".","The","functino","should","return","a","tf.Tensor",".","hidden_state_init_trainable","(","bool",")",":","Bool","for","whether","the","initial","hidden","state","is","trainable",".","learn_std","(","bool",")",":","Is","std","trainable",".","std_share_network","(","bool",")",":","Boolean","for","whether","mean","and","std","share","the","same","network",".","init_std","(","float",")",":","Initial","value","for","std",".","layer_normalization","(","bool",")",":","Bool","for","using","layer","normalization","or","not",".","state_include_action","(","bool",")",":","Whether","the","state","includes","action",".","If","True",",","input","dimension","will","be","(","observation","dimension","+","action","dimension",")","."]
docstr: int: Dimension of the policy input.
docstr_tok: ["int",":","Dimension","of","the","policy","input","."]
code: def input_dim(self):
      """int: Dimension of the policy input."""
      return self._input_dim
  
imports: import akro
  import numpy
  import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models import GaussianGRUModel
  from garage.tf.policies.policy import Policy
label_desc: Use image node_id 4 for calling the GaussianGRUPolicy obj's underlying member method code with example usage: obj.input_dim() and returns: self