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: Network input spec.
  
  Return:
      list[str]: List of key(str) for the network outputs.
code: def network_input_spec(self):
      """Network input spec.
  
      Return:
          list[str]: List of key(str) for the network outputs.
  
      """
      return ["input_var1", "input_var2"]
  
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 2 for calling the MLPMergeModel obj's underlying member method code with example usage: obj.network_input_spec() and returns: list