class_docstr: Fits a Gaussian distribution to the outputs of a CNN.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      filters (Tuple[Tuple[int, Tuple[int, int]], ...]): Number and dimension
          of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there
          are two convolutional layers. The filter for the first layer have 3
          channels and its shape is (3 x 5), while the filter for the second
          layer have 32 channels and its shape is (3 x 3).
      strides(tuple[int]): The stride of the sliding window. For example,
          (1, 2) means there are two convolutional layers. The stride of the
          filter for first layer is 1 and that of the second layer is 2.
      padding (str): The type of padding algorithm to use,
          either 'SAME' or 'VALID'.
      name (str): Model name, also the variable scope.
      hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the Convolutional model for mean. For example, (32, 32) means the
          network consists of two dense 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.
      name (str): Name of this model (also used as its scope).
      learn_std (bool): Whether to train the standard deviation parameter of
          the Gaussian distribution.
      init_std (float): Initial standard deviation for the Gaussian
          distribution.
      adaptive_std (bool): Whether to use a neural network to learn the
          standard deviation of the Gaussian distribution. Unless True, the
          standard deviation is learned as a parameter which is not
          conditioned on the inputs.
      std_share_network (bool): Boolean for whether the mean and standard
          deviation models share a CNN network. If True, each is a head from
          a single body network. Otherwise, the parameters are estimated
          using the outputs of two indepedent networks.
      std_filters (Tuple[Tuple[int, Tuple[int, int]], ...]): Number and
          dimension of filters. For example, ((3, (3, 5)), (32, (3, 3)))
          means there are two convolutional layers. The filter for the first
          layer have 3 channels and its shape is (3 x 5), while the filter
          for the second layer have 32 channels and its shape is (3 x 3).
      std_strides(tuple[int]): The stride of the sliding window. For example,
          (1, 2) means there are two convolutional layers. The stride of the
          filter for first layer is 1 and that of the second layer is 2.
      std_padding (str): The type of padding algorithm to use in std network,
          either 'SAME' or 'VALID'.
      std_hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the Conv for std. For example, (32, 32) means the Conv consists
          of two hidden layers, each with 32 hidden units.
      std_hidden_nonlinearity (callable): Nonlinearity for each hidden layer
          in the std network.
      std_output_nonlinearity (Callable): Activation function for output
          dense layer in the std network. It should return a tf.Tensor. Set
          it to None to maintain a linear activation.
      layer_normalization (bool): Bool for using layer normalization or not.
      normalize_inputs (bool): Bool for normalizing inputs or not.
      normalize_outputs (bool): Bool for normalizing outputs or not.
      subsample_factor (float): The factor to subsample the data. By default
          it is 1.0, which means using all the data.
      optimizer (garage.tf.Optimizer): Optimizer used for fitting the model.
      optimizer_args (dict): Arguments for the optimizer. Default is None,
          which means no arguments.
      use_trust_region (bool): Whether to use a KL-divergence constraint.
      max_kl_step (float): KL divergence constraint for each iteration, if
          `use_trust_region` is active.
code: def __init__(
      self,
      env_spec,
      filters,
      strides,
      padding,
      hidden_sizes,
      hidden_nonlinearity=tf.nn.tanh,
      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(),
      name="GaussianCNNBaseline",
      learn_std=True,
      init_std=1.0,
      adaptive_std=False,
      std_share_network=False,
      std_filters=(),
      std_strides=(),
      std_padding="SAME",
      std_hidden_sizes=(),
      std_hidden_nonlinearity=None,
      std_output_nonlinearity=None,
      layer_normalization=False,
      normalize_inputs=True,
      normalize_outputs=True,
      subsample_factor=1.0,
      optimizer=None,
      optimizer_args=None,
      use_trust_region=True,
      max_kl_step=0.01,
  ):
      if not isinstance(
          env_spec.observation_space, akro.Box
      ) or not len(env_spec.observation_space.shape) in (2, 3):
          raise ValueError(
              "{} can only process 2D, 3D akro.Image or"
              " akro.Box observations, but received an env_spec with "
              "observation_space of type {} and shape {}".format(
                  type(self).__name__,
                  type(env_spec.observation_space).__name__,
                  env_spec.observation_space.shape,
              )
          )
  
      self._env_spec = env_spec
      self._use_trust_region = use_trust_region
      self._subsample_factor = subsample_factor
      self._max_kl_step = max_kl_step
      self._normalize_inputs = normalize_inputs
      self._normalize_outputs = normalize_outputs
  
      if optimizer_args is None:
          optimizer_args = dict()
      if optimizer is None:
          if use_trust_region:
              self._optimizer = make_optimizer(
                  PenaltyLBFGSOptimizer, **optimizer_args
              )
          else:
              self._optimizer = make_optimizer(
                  LBFGSOptimizer, **optimizer_args
              )
      else:
          self._optimizer = make_optimizer(optimizer, **optimizer_args)
  
      super().__init__(
          input_dim=env_spec.observation_space.shape,
          output_dim=1,
          filters=filters,
          strides=strides,
          padding=padding,
          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,
          learn_std=learn_std,
          adaptive_std=adaptive_std,
          std_share_network=std_share_network,
          init_std=init_std,
          min_std=None,
          max_std=None,
          std_filters=std_filters,
          std_strides=std_strides,
          std_padding=std_padding,
          std_hidden_sizes=std_hidden_sizes,
          std_hidden_nonlinearity=std_hidden_nonlinearity,
          std_output_nonlinearity=std_output_nonlinearity,
          std_parameterization="exp",
          layer_normalization=layer_normalization,
          name=name,
      )
      # model for old distribution, used when trusted region is on
      self._old_model = self.clone_model(name=name + "_old_model")
      self._old_network = None
  
      self._x_mean = None
      self._x_std = None
      self._y_mean = None
      self._y_std = None
  
      self._initialize()
  
imports: import akro
  from dowel import tabular
  import numpy
  import tensorflow
  from garage import make_optimizer
  from garage.experiment import deterministic
  from garage.np.baselines.baseline import Baseline
  from garage.tf import compile_function
  from garage.tf.baselines.gaussian_cnn_baseline_model import GaussianCNNBaselineModel
  from garage.tf.optimizers import LBFGSOptimizer, PenaltyLBFGSOptimizer
label_desc: Use image node_id 1 to create a new GaussianCNNBaseline object from inherited base classes: GaussianCNNBaselineModel, Baseline with example: obj = GaussianCNNBaseline(env_spec, filters, strides, padding, hidden_sizes, hidden_nonlinearity, hidden_w_init, hidden_b_init, output_nonlinearity, output_w_init, output_b_init, name, learn_std, init_std, adaptive_std, std_share_network, std_filters, std_strides, std_padding, std_hidden_sizes, std_hidden_nonlinearity, std_output_nonlinearity, layer_normalization, normalize_inputs, normalize_outputs, subsample_factor, optimizer, optimizer_args, use_trust_region, max_kl_step)