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.
docstr: Object.__getstate__.
  
  Returns:
      dict: The state to be pickled for the instance.
docstr_tok: ["Object.__getstate__",".","Returns",":","dict",":","The","state","to","be","pickled","for","the","instance","."]
code: def __getstate__(self):
      """Object.__getstate__.
  
      Returns:
          dict: The state to be pickled for the instance.
  
      """
      new_dict = super().__getstate__()
      del new_dict["_f_predict"]
      del new_dict["_old_network"]
      del new_dict["_x_mean"]
      del new_dict["_x_std"]
      del new_dict["_y_mean"]
      del new_dict["_y_std"]
      return new_dict
  
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 8 for calling the GaussianCNNBaseline obj's underlying member method code with example usage: obj.__getstate__() and returns: new_dict