class_docstr: GRU Model.
  
  Args:
      output_dim (int): Dimension of the network output.
      hidden_dim (int): Hidden dimension for GRU cell.
      name (str): Policy name, also the variable scope.
      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.
      layer_normalization (bool): Bool for using layer normalization or not.
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docstr: Build model given input placeholder(s).
  
  Args:
      all_input_var (tf.Tensor): Place holder for entire time-series
          inputs.
      step_input_var (tf.Tensor): Place holder for step inputs.
      step_hidden_var (tf.Tensor): Place holder for step hidden state.
      name (str): Inner model name, also the variable scope of the
          inner model, if exist. One example is
          garage.tf.models.Sequential.
  
  Return:
      tf.Tensor: Entire time-series outputs.
      tf.Tensor: Step output.
      tf.Tensor: Step hidden state.
      tf.Tensor: Initial hidden state.
docstr_tok: ["Build","model","given","input","placeholder","(","s",")",".","Args",":","all_input_var","(","tf.Tensor",")",":","Place","holder","for","entire","time-series","inputs",".","step_input_var","(","tf.Tensor",")",":","Place","holder","for","step","inputs",".","step_hidden_var","(","tf.Tensor",")",":","Place","holder","for","step","hidden","state",".","name","(","str",")",":","Inner","model","name",",","also","the","variable","scope","of","the","inner","model",",","if","exist",".","One","example","is","garage.tf.models.Sequential",".","Return",":","tf.Tensor",":","Entire","time-series","outputs",".","tf.Tensor",":","Step","output",".","tf.Tensor",":","Step","hidden","state",".","tf.Tensor",":","Initial","hidden","state","."]
code: def _build(
      self, all_input_var, step_input_var, step_hidden_var, name=None
  ):
      """Build model given input placeholder(s).
  
      Args:
          all_input_var (tf.Tensor): Place holder for entire time-series
              inputs.
          step_input_var (tf.Tensor): Place holder for step inputs.
          step_hidden_var (tf.Tensor): Place holder for step hidden state.
          name (str): Inner model name, also the variable scope of the
              inner model, if exist. One example is
              garage.tf.models.Sequential.
  
      Return:
          tf.Tensor: Entire time-series outputs.
          tf.Tensor: Step output.
          tf.Tensor: Step hidden state.
          tf.Tensor: Initial hidden state.
  
      """
      del name
      return gru(
          name="gru",
          gru_cell=self._gru_cell,
          all_input_var=all_input_var,
          step_input_var=step_input_var,
          step_hidden_var=step_hidden_var,
          hidden_state_init=self._hidden_state_init,
          hidden_state_init_trainable=self._hidden_state_init_trainable,
          output_nonlinearity_layer=self._output_nonlinearity_layer,
      )
  
file_path: garage/src/garage/tf/models/gru_model.py
imports: import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models.gru import gru
  from garage.tf.models.model import Model
label_desc: Use image node_id 5 for calling the GRUModel obj's underlying member method code with example usage: obj._build(all_input_var, step_input_var, step_hidden_var, name) and returns: gru