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 _initialize(self):
      input_var = tf.compat.v1.placeholder(
          tf.float32, shape=(None,) + self._input_shape
      )
      if isinstance(self.env_spec.observation_space, akro.Image):
          input_var = tf.cast(input_var, tf.float32) / 255.0
  
      ys_var = tf.compat.v1.placeholder(
          dtype=tf.float32, name="ys", shape=(None, self._output_dim)
      )
  
      self._old_network = self._old_model.build(input_var)
      (
          _,
          _,
          norm_dist,
          norm_mean,
          norm_log_std,
          _,
          mean,
          _,
          self._x_mean,
          self._x_std,
          self._y_mean,
          self._y_std,
      ) = self.build(input_var).outputs
  
      normalized_ys_var = (ys_var - self._y_mean) / self._y_std
      old_normalized_dist = self._old_network.normalized_dist
  
      mean_k1 = tf.reduce_mean(
          old_normalized_dist.kl_divergence(norm_dist)
      )
      loss = -tf.reduce_mean(norm_dist.log_prob(normalized_ys_var))
  
      self._f_predict = compile_function([input_var], mean)
  
      optimizer_args = dict(
          loss=loss,
          target=self,
          network_outputs=[norm_mean, norm_log_std],
      )
  
      if self._use_trust_region:
          optimizer_args["leq_constraint"] = (
              mean_k1,
              self._max_kl_step,
          )
          optimizer_args["inputs"] = [input_var, ys_var]
      else:
          optimizer_args["inputs"] = [input_var, ys_var]
  
      with tf.name_scope("update_opt"):
          self._optimizer.update_opt(**optimizer_args)
  
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 2 for calling the GaussianCNNBaseline obj's underlying member method code with example usage: obj._initialize() without return types