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.
code: def __init__(
      self,
      input_shape,
      output_dim,
      name="NormalizedInputMLPModel",
      hidden_sizes=(32, 32),
      hidden_nonlinearity=tf.nn.relu,
      hidden_w_init=tf.initializers.glorot_uniform(
          seed=deterministic.get_tf_seed_stream()
      ),
      hidden_b_init=tf.zeros_initializer(),
      output_nonlinearity=None,
      output_w_init=tf.initializers.glorot_uniform(
          seed=deterministic.get_tf_seed_stream()
      ),
      output_b_init=tf.zeros_initializer(),
      layer_normalization=False,
  ):
      super().__init__(
          output_dim=output_dim,
          name=name,
          hidden_sizes=hidden_sizes,
          hidden_nonlinearity=hidden_nonlinearity,
          hidden_w_init=hidden_w_init,
          hidden_b_init=hidden_b_init,
          output_nonlinearity=output_nonlinearity,
          output_w_init=output_w_init,
          output_b_init=output_b_init,
          layer_normalization=layer_normalization,
      )
      self._input_shape = input_shape
  
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 1 to create a new NormalizedInputMLPModel object from inherited base classes: MLPModel with example: obj = NormalizedInputMLPModel(input_shape, output_dim, name, hidden_sizes, hidden_nonlinearity, hidden_w_init, hidden_b_init, output_nonlinearity, output_w_init, output_b_init, layer_normalization)