class_docstr: MLP Merge Model.
  
  Args:
      output_dim (int): Dimension of the network output.
      name (str): Model name, also the variable scope.
      hidden_sizes (list[int]): Output dimension of dense layer(s).
          For example, (32, 32) means this MLP consists of two
          hidden layers, each with 32 hidden units.
      concat_layer (int): The index of layers at which to concatenate
          input_var2 with the network. The indexing works like standard
          python list indexing. Index of 0 refers to the input layer
          (input_var1) while an index of -1 points to the last hidden
          layer. Default parameter points to second layer from the end.
      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.
docstr: Build model given input placeholder(s).
  
  Args:
      state_input (tf.Tensor): Tensor input for state.
      action_input (tf.Tensor): Tensor input for action.
      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: Tensor output of the model.
code: def _build(self, state_input, action_input, name=None):
      """Build model given input placeholder(s).
  
      Args:
          state_input (tf.Tensor): Tensor input for state.
          action_input (tf.Tensor): Tensor input for action.
          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: Tensor output of the model.
  
      """
      del name
      return mlp(
          input_var=state_input,
          output_dim=self._output_dim,
          hidden_sizes=self._hidden_sizes,
          input_var2=action_input,
          concat_layer=self._concat_layer,
          name="mlp_concat",
          hidden_nonlinearity=self._hidden_nonlinearity,
          hidden_w_init=self._hidden_w_init,
          hidden_b_init=self._hidden_b_init,
          output_nonlinearity=self._output_nonlinearity,
          output_w_init=self._output_w_init,
          output_b_init=self._output_b_init,
          layer_normalization=self._layer_normalization,
      )
  
file_path: garage/src/garage/tf/models/mlp_merge_model.py
imports: import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models.mlp import mlp
  from garage.tf.models.model import Model
label_desc: Use image node_id 3 for calling the MLPMergeModel obj's underlying member method code with example usage: obj._build(state_input, action_input, name) and returns: mlp