def __init__(self, num_inputs, num_hidden_layers, num_inner_features):
    'Initializer for linear model.\n\n        Args:\n            num_inputs: the dimension of input data.\n            num_hidden_layers: the number of hidden layers.\n            num_inner_features: the number of features in the hidden layers\n        '
    super(NNModel, self).__init__()
    self.input_layer = nn.Linear(num_inputs, num_inner_features)
    hidden_layers = []
    for _ in range(num_hidden_layers):
        hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))
        hidden_layers.append(nn.ReLU())
    self.hidden_layers = nn.Sequential(*hidden_layers)
    self.output_layer = nn.Linear(num_inner_features, 1)
Initializer for linear model.

Args:
    num_inputs: the dimension of input data.
    num_hidden_layers: the number of hidden layers.
    num_inner_features: the number of features in the hidden layers
def __init__(self, num_inputs, num_hidden_layers, num_inner_features):
    'Initializer for linear model.\n\n        Args:\n            num_inputs: the dimension of input data.\n            num_hidden_layers: the number of hidden layers.\n            num_inner_features: the number of features in the hidden layers\n        '
    super(NNModel, self).__init__()
    self.input_layer = nn.Linear(num_inputs, num_inner_features)
    hidden_layers = []
    for _ in range(num_hidden_layers):
        hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))
        hidden_layers.append(nn.ReLU())
    self.hidden_layers = nn.Sequential(*hidden_layers)
    self.output_layer = nn.Linear(num_inner_features, 1)