class_docstr: NormalizedInputMLPModel based on garage.tf.models.Model class.
  
  This class normalized the inputs and pass the normalized input to a
  MLP model, which can be used to perform linear regression to the outputs.
  
  Args:
      input_shape (tuple[int]): Input shape of the training data.
      output_dim (int): Output dimension of the model.
      name (str): Model name, also the variable scope.
      hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the MLP for mean. For example, (32, 32) means the MLP 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.
docstr: Network output spec.
  
  Return:
      list[str]: List of key(str) for the network outputs.
code: def network_output_spec(self):
      """Network output spec.
  
      Return:
          list[str]: List of key(str) for the network outputs.
  
      """
      return ["y_hat", "x_mean", "x_std"]
  
file_path: garage/src/garage/tf/models/normalized_input_mlp_model.py
imports: import numpy
  import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models.mlp_model import MLPModel
label_desc: Use image node_id 2 for calling the NormalizedInputMLPModel obj's underlying member method code with example usage: obj.network_output_spec() and returns: list