def Func(arg_0, arg_1, arg_2):
        """Run GIES on an undirected graph.

        Args:
            data (pandas.DataFrame): DataFrame containing the data
            graph (networkx.Graph): Skeleton of the graph to orient

        Returns:
            networkx.DiGraph: Solution given by the GIES algorithm.

        """
        # Building setup w/ arguments.
        arg_0.arguments['{VERBOSE}'] = str(arg_0.verbose).upper()
        arg_0.arguments['{SCORE}'] = arg_0.scores[arg_0.score]

        arg_4 = DataFrame(nx.adj_matrix(arg_2, weight=None).todense())
        arg_5 = DataFrame(1 - arg_4.values)

        arg_6 = arg_0._run_gies(arg_1, fixedGaps=arg_5, verbose=arg_0.verbose)

        return nx.relabel_nodes(nx.DiGraph(arg_6),
                                {arg_7: arg_8 for arg_7, arg_8 in enumerate(arg_1.columns)})


def Func(arg_0, arg_1):
        """Feed-Func through the network."""
        return th.nn.functional.linear(arg_1, arg_0.weight.div(arg_0.weight.pow(2).sum(0).sqrt()))


def Func(arg_0, arg_1, arg_2=None, arg_3=6, arg_4=None, arg_5=0, arg_6=None,
                arg_7=False, arg_8=False, arg_9=False):
        """Execute SAM on a dataset given a skeleton or not.

        Args:
            data (pandas.DataFrame): Observational data for estimation of causal relationships by SAM
            skeleton (numpy.ndarray): A priori knowledge about the causal relationships as an adjacency matrix.
                      Can be fed either directed or undirected links.
            nruns (int): Number of runs to be made for causal estimation.
                   Recommended: >=12 for optimal performance.
            njobs (int): Numbers of jobs to be run in Parallel.
                   Recommended: 1 if no GPU available, 2*number of GPUs else.
            gpus (int): Number of available GPUs for the algorithm.
            verbose (bool): verbose mode
            plot (bool): Plot losses interactively. Not recommended if nruns>1
            plot_generated_pair (bool): plots a generated pair interactively.  Not recommended if nruns>1
        Returns:
            networkx.DiGraph: Graph estimated by SAM, where A[i,j] is the term
            of the ith variable for the jth generator.
        """
        arg_6, arg_4 = SETTINGS.get_default(('verbose', arg_6), ('nb_jobs', arg_4))
        if arg_4 != 1:
            arg_10 = Parallel(n_jobs=arg_4)(delayed(run_SAM)(arg_1,
                                                               skeleton=arg_2,
                                                               lr_gen=arg_0.lr, lr_disc=arg_0.dlr,
                                                               regul_param=arg_0.l1, nh=arg_0.nh, dnh=arg_0.dnh,
                                                               gpu=bool(arg_5), train_epochs=arg_0.train,
                                                               test_epochs=arg_0.test, batch_size=arg_0.batchsize,
                                                               arg_7=arg_7, arg_6=arg_6, gpu_no=arg_13 % max(arg_5, 1))
                                              for arg_13 in range(arg_3))
        else:
            arg_10 = [run_SAM(arg_1, skeleton=arg_2,
                                lr_gen=arg_0.lr, lr_disc=arg_0.dlr,
                                regul_param=arg_0.l1, nh=arg_0.nh, dnh=arg_0.dnh,
                                gpu=bool(arg_5), train_epochs=arg_0.train,
                                test_epochs=arg_0.test, batch_size=arg_0.batchsize,
                                arg_7=arg_7, arg_6=arg_6, gpu_no=0)
                        for arg_13 in range(arg_3)]
        if arg_9:
            return arg_10
        else:
            arg_11 = arg_10[0]
            for arg_12 in arg_10[1:]:
                arg_11 += arg_12
            arg_11 /= arg_3
        return nx.relabel_nodes(nx.DiGraph(arg_11), {arg_13: arg_14 for arg_13, arg_14 in enumerate(arg_1.columns)})


def Func(arg_0, arg_1, arg_2, **arg_3):
        """ Infer causal relationships between 2 variables using the CDS statistic

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2

        Returns:
            float: Causation score (Value : 1 if a->b and -1 if b->a)
        """
        return arg_0.cds_score(arg_2, arg_1) - arg_0.cds_score(arg_1, arg_2)


def Func(arg_0, arg_1, arg_2, **arg_3):
        """Prediction method for pairwise causal inference using the ANM model.

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2

        Returns:
            float: Causation score (Value : 1 if a->b and -1 if b->a)
        """
        arg_1 = scale(arg_1).reshape((-1, 1))
        arg_2 = scale(arg_2).reshape((-1, 1))

        return arg_0.anm_score(arg_2, arg_1) - arg_0.anm_score(arg_1, arg_2)


def Func(arg_0, arg_1, arg_2):
        """Compute the fitness score of the ANM model in the x->y direction.

        Args:
            a (numpy.ndarray): Variable seen as cause
            b (numpy.ndarray): Variable seen as effect

        Returns:
            float: ANM fit score
        """
        arg_3 = GaussianProcessRegressor().fit(arg_1, arg_2)
        arg_4 = arg_3.predict(arg_1)
        arg_5 = normalized_hsic(arg_4 - arg_2, arg_1)

        return arg_5


def Func(arg_0, arg_1, arg_2=0.01, arg_3=2000, **arg_4):
        """ Predict the graph skeleton.

        Args:
            data (pandas.DataFrame): observational data
            alpha (float): regularization parameter
            max_iter (int): maximum number of iterations

        Returns:
            networkx.Graph: Graph skeleton
        """
        arg_5 = GraphLasso(arg_2=arg_2, arg_3=arg_3)
        arg_5.fit(arg_1.values)

        return nx.relabel_nodes(nx.DiGraph(arg_5.get_precision()),
                                {arg_6: arg_7 for arg_6, arg_7 in enumerate(arg_1.columns)})


def Func(arg_0):
    """Autoset GPU parameters using CUDA_VISIBLE_DEVICES variables.

    Return default config if variable not set.
    :param set_var: Variable to set. Must be of type ConfigSettings
    """
    try:
        arg_1 = ast.literal_eval(os.environ["CUDA_VISIBLE_DEVICES"])
        if type(arg_1) != list and type(arg_1) != tuple:
            arg_1 = [arg_1]
        if len(arg_1) != 0:
            arg_0.GPU = len(arg_1)
            arg_0.NB_JOBS = len(arg_1)
            warnings.warn("Detecting CUDA devices : {}".format(arg_1))

    except KeyError:
        arg_0.GPU = check_cuda_devices()
        arg_0.NB_JOBS = arg_0.GPU
        warnings.warn("Detecting {} CUDA devices.".format(arg_0.GPU))
        if not arg_0.GPU:
            warnings.warn("No GPU automatically detected. Setting SETTINGS.GPU to 0, " +
                          "and SETTINGS.NB_JOBS to cpu_count.")
            arg_0.GPU = 0
            arg_0.NB_JOBS = multiprocessing.cpu_count()

    return arg_0


def Func(arg_0, arg_1, *arg_2, **arg_3):
        """Generic Func method, chooses which subfunction to use for a more
        suited.

        Depending on the type of `x` and of `*args`, this function process to execute
        different functions in the priority order:

        1. If ``args[0]`` is a ``networkx.(Di)Graph``, then ``self.orient_graph`` is executed.
        2. If ``args[0]`` exists, then ``self.Func_proba`` is executed.
        3. If ``x`` is a ``pandas.DataFrame``, then ``self.Func_dataset`` is executed.
        4. If ``x`` is a ``pandas.Series``, then ``self.Func_proba`` is executed.

        Args:
            x (numpy.array or pandas.DataFrame or pandas.Series): First variable or dataset.
            args (numpy.array or networkx.Graph): graph or second variable.

        Returns:
            pandas.Dataframe or networkx.Digraph: Funcions output
        """
        if len(arg_2) > 0:
            if type(arg_2[0]) == nx.Graph or type(arg_2[0]) == nx.DiGraph:
                return arg_0.orient_graph(arg_1, *arg_2, **arg_3)
            else:
                return arg_0.Func_proba(arg_1, *arg_2, **arg_3)
        elif type(arg_1) == DataFrame:
            return arg_0.Func_dataset(arg_1, *arg_2, **arg_3)
        elif type(arg_1) == Series:
            return arg_0.Func_proba(arg_1.iloc[0], arg_1.iloc[1], *arg_2, **arg_3)


def Func(arg_0, arg_1, **arg_2):
        """Generic dataset prediction function.

        Runs the score independently on all pairs.

        Args:
            x (pandas.DataFrame): a CEPC format Dataframe.
            kwargs (dict): additional arguments for the algorithms

        Returns:
            pandas.DataFrame: a Dataframe with the predictions.
        """
        arg_3 = arg_2.get("printout", None)
        arg_4 = []
        arg_5 = []
        arg_1.columns = ["A", "B"]
        for arg_7, arg_8 in arg_1.iterrows():
            arg_9 = scale(arg_8['A'].reshape((len(arg_8['A']), 1)))
            arg_10 = scale(arg_8['B'].reshape((len(arg_8['B']), 1)))

            arg_4.append(arg_0.predict_proba(arg_9, arg_10, arg_7=arg_7))

            if arg_3 is not None:
                arg_5.append([arg_8['SampleID'], arg_4[-1]])
                DataFrame(arg_5, arg_6=['SampleID', 'Predictions']).to_csv(
                    arg_3, index=False)
        return arg_4


def Func(arg_0, arg_1, arg_2):
        """Run the algorithm on a directed_graph.

        Args:
            data (pandas.DataFrame): DataFrame containing the data
            graph (networkx.DiGraph): Skeleton of the graph to orient

        Returns:
            networkx.DiGraph: Solution on the given skeleton.

        .. warning::
           The algorithm is ran on the skeleton of the given graph.

        """
        warnings.warn("The algorithm is ran on the skeleton of the given graph.")
        return arg_0.orient_undirected_graph(arg_1, nx.Graph(arg_2))


def Func(arg_0):
    """Compute the gaussian kernel on a 1D vector."""
    arg_1 = np.power(euclidean_distances(arg_0, arg_0), 2)
    return np.exp(-arg_1 / (2.0))


def Func(arg_0):
    """Init a noise variable."""
    return np.random.rand(1) * np.random.randn(arg_0, 1) \
        + random.sample([2, -2], 1)


def Func(arg_0, arg_1):
        """Runs Jarfo independently on all pairs.

        Args:
            x (pandas.DataFrame): a CEPC format Dataframe.
            kwargs (dict): additional arguments for the algorithms

        Returns:
            pandas.DataFrame: a Dataframe with the predictions.
        """
        if len(list(arg_1.columns)) == 2:
            arg_1.columns = ["A", "B"]
        if arg_0.model is None:
            raise AssertionError("Model has not been trained before predictions")
        arg_3 = DataFrame()

        for arg_4, arg_5 in arg_1.iterrows():
            arg_3 = arg_3.append(arg_5, ignore_index=True)
            arg_3 = arg_3.append({'A': arg_5["B"], 'B': arg_5["A"]}, ignore_index=True)
        return predict.predict(deepcopy(arg_3), deepcopy(arg_0.model))[::2]


def Func(arg_0, arg_1, arg_2, arg_3=0, **arg_4):
        """ Use Jarfo to predict the causal direction of a pair of vars.

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2
            idx (int): (optional) index number for printing purposes

        Returns:
            float: Causation score (Value : 1 if a->b and -1 if b->a)
        """
        return arg_0.predict_dataset(DataFrame([[arg_1, arg_2]],
                                              columns=['A', 'B']))


def Func(arg_0, **arg_1):
    """Implementation of the ARACNE algorithm.

    Args:
     mat (numpy.ndarray): matrix, if it is a square matrix, the program assumes
         it is a relevance matrix where mat(i,j) represents the similarity content
         between nodes i and j. Elements of matrix should be
         non-negative.

    Returns:
    mat_nd (numpy.ndarray): Output deconvolved matrix (direct dependency matrix). Its components
        represent direct edge weights of observed interactions.

    .. note::
       Ref: ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
       Adam A Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera and Andrea Califano
       DOI: https://doi.org/10.1186/1471-2105-7-S1-S7
    """
    arg_2 = arg_1.get('I0', 0.0)  # No default thresholding
    arg_3 = arg_1.get('W0', 0.05)

    # thresholding
    arg_0 = np.where(arg_0 > arg_2, arg_0, 0)

    # Finding triplets and filtering them
    for arg_4 in range(arg_0.shape[0]-2):
        for arg_5 in range(arg_4+1, arg_0.shape[0]-1):
            for arg_6 in range(arg_5+1, arg_0.shape[0]):
                arg_7 = [arg_0[arg_4, arg_5], arg_0[arg_5, arg_6], arg_0[arg_4, arg_6]]
                arg_8, arg_9 = min(enumerate(arg_7), key=operator.itemgetter(1))
                if 0 < arg_9 < arg_3:
                    if arg_8 == 0:
                        arg_0[arg_4, arg_5] = arg_0[arg_5, arg_4] = 0.
                    elif arg_8 == 1:
                        arg_0[arg_5, arg_6] = arg_0[arg_6, arg_5] = 0.
                    else:
                        arg_0[arg_4, arg_6] = arg_0[arg_6, arg_4] = 0.
    return arg_0


def Func(arg_0, arg_1="aracne", **arg_2):
    """Apply deconvolution to a networkx graph.

    Args:
       g (networkx.Graph): Graph to apply deconvolution to
       alg (str): Algorithm to use ('aracne', 'clr', 'nd')
       kwargs (dict): extra options for algorithms

    Returns:
       networkx.Graph: graph with undirected links removed.
    """
    arg_1 = {"aracne": aracne,
           "nd": network_deconvolution,
           "clr": clr}[arg_1]
    arg_3 = np.array(nx.adjacency_matrix(arg_0).todense())
    return nx.relabel_nodes(nx.DiGraph(arg_1(arg_3, **arg_2)),
                            {arg_4: arg_5 for arg_4, arg_5 in enumerate(list(arg_0.nodes()))})


def Func(arg_0):
    """Input a graph and output a DAG.

    The heuristic is to reverse the edge with the lowest score of the cycle
    if possible, else remove it.

    Args:
        g (networkx.DiGraph): Graph to modify to output a DAG

    Returns:
        networkx.DiGraph: DAG made out of the input graph.
    """
    while not nx.is_directed_acyclic_graph(arg_0):
        arg_1 = next(nx.simple_cycles(arg_0))
        arg_2 = []
        arg_3 = []
        for arg_4, arg_5 in zip(arg_1[:1], arg_1[:1]):
            arg_3.append((arg_4, arg_5))
            arg_2.append(arg_0[arg_4][arg_5]['weight'])

        arg_4, arg_5 = arg_3[arg_2.index(min(arg_2))]
        arg_6 = deepcopy(arg_0)
        arg_6.remove_edge(arg_4, arg_5)
        arg_6.add_edge(arg_5, arg_4)

        if len(list(nx.simple_cycles(arg_6))) < len(list(nx.simple_cycles(arg_0))):
            arg_0.add_edge(arg_5, arg_4, weight=min(arg_2))
        arg_0.remove_edge(arg_4, arg_5)
    return arg_0


def Func(arg_0, arg_1):
    """
    Returns the weighted average and standard deviation.

    values, weights -- numpy ndarrays with the same shape.
    """
    arg_2 = np.average(arg_0, arg_1=arg_1, axis=0)
    arg_3 = np.dot(arg_1, (arg_0 - arg_2) ** 2) / arg_1.sum()  # Fast and numerically precise
    return (arg_2, np.sqrt(arg_3))


def Func(arg_0, arg_1):
        """Pass data through the net structure.

        :param x: input data: shape (:,1)
        :type x: torch.Variable
        :return: output of the shallow net
        :rtype: torch.Variable

        """
        arg_0.noise.normal_()
        return arg_0.layers(th.cat([arg_1, arg_0.noise], 1))


def Func(arg_0, arg_1, arg_2, arg_3=0.01, arg_4=1000, arg_5=1000, arg_6=0, arg_7=None, **arg_8):
        """Run the GNN on a pair x,y of FloatTensor data."""
        arg_7 = SETTINGS.get_default(arg_7=arg_7)
        arg_9 = th.optim.Adam(arg_0.parameters(), arg_3=arg_3)
        arg_10 = 0
        arg_11 = 0

        for arg_12 in range(arg_4 + arg_5):
            arg_9.zero_grad()
            arg_13 = arg_0.forward(arg_1)
            arg_14 = arg_0.criterion(arg_13, arg_2)
            arg_10 += arg_14.item()

            if arg_12 < arg_4:
                arg_14.backward()
                arg_9.step()
            else:
                arg_11 += arg_10

            # print statistics
            if arg_7 and not arg_12 % 300:
                print('Idx:{}; epoch:{}; score:{}'.
                      format(arg_6, arg_12, arg_10/300))
                arg_10 = 0.0

        return arg_11 / arg_5


def Func(arg_0, arg_1, arg_2, arg_3=6, arg_4=None, arg_5=None,
                      arg_6=0, arg_7=None, arg_8=0.01,
                      arg_9=16, arg_10=1000, arg_11=1000):
        """Run multiple times GNN to estimate the causal direction.

        Args:
            a (np.ndarray): Variable 1
            b (np.ndarray): Variable 2
            nb_runs (int): number of runs to execute per batch (before testing for significance with t-test).
            nb_jobs (int): number of runs to execute in parallel. (Initialized with ``cdt.SETTINGS.NB_JOBS``)
            gpu (bool): use gpu (Initialized with ``cdt.SETTINGS.GPU``)
            idx (int): (optional) index of the pair, for printing purposes
            verbose (bool): verbosity (Initialized with ``cdt.SETTINGS.verbose``)
            ttest_threshold (float): threshold to stop the boostraps before ``nb_max_runs`` if the difference is significant
            nb_max_runs (int): Max number of bootstraps
            train_epochs (int): Number of epochs during which the model is going to be trained
            test_epochs (int): Number of epochs during which the model is going to be tested

        Returns:
            float: Causal score of the pair (Value : 1 if a->b and -1 if b->a)
        """
        arg_12, arg_7, arg_5 = SETTINGS.get_default(('nb_jobs', arg_4), ('verbose', arg_7), ('gpu', arg_5))
        arg_13 = np.stack([arg_1.ravel(), arg_2.ravel()], 1)
        arg_14 = TTestCriterion(
            max_iter=arg_9, runs_per_iter=arg_3, threshold=arg_8)

        arg_15 = []
        arg_16 = []

        while arg_14.loop(arg_15, arg_16):
            if arg_4 != 1:
                arg_17 = Parallel(n_jobs=arg_4)(delayed(GNN_instance)(
                    arg_13, arg_6=arg_6, device='cuda:{}'.format(run % arg_5) if arg_5 else 'cpu',
                    arg_7=arg_7, arg_10=arg_10, arg_11=arg_11) for run in range(arg_14.iter, arg_14.iter + arg_3))
            else:
                arg_17 = [GNN_instance(arg_13, arg_6=arg_6,
                                            device='cuda:0' if arg_5 else 'cpu',
                                            arg_7=arg_7,
                                            arg_10=arg_10,
                                            arg_11=arg_11)
                               for run in range(arg_14.iter, arg_14.iter + arg_3)]
            arg_15.extend([arg_18[0] for arg_18 in arg_17])
            arg_16.extend([arg_18[1] for arg_18 in arg_17])

        if arg_7:
            print("P-value after {} runs : {}".format(arg_14.iter,
                                                      arg_14.p_value))

        arg_19 = np.mean(arg_15)
        arg_20 = np.mean(arg_16)

        return (arg_20 - arg_19) / (arg_20 + arg_19)


def Func(arg_0, arg_1):
        """Passing data through the network.

        :param x: 2d tensor containing both (x,y) Variables
        :return: output of the net
        """

        arg_2 = arg_0.conv(arg_1).mean(dim=2)
        return arg_0.dense(arg_2)


def Func(arg_0, **arg_1):
        """Updates all rows that match the filter."""

        # build up the query to execute
        arg_0._for_write = True
        if django.VERSION >= (2, 0):
            arg_3 = arg_0.query.chain(UpdateQuery)
        else:
            arg_3 = arg_0.query.clone(UpdateQuery)
        arg_3._annotations = None
        arg_3.add_Func_values(arg_1)

        # build the compiler for for the query
        arg_5 = django.db.connections[arg_0.db]
        arg_6 = PostgresReturningUpdateCompiler(arg_3, arg_5, arg_0.db)

        # execute the query
        with transaction.atomic(using=arg_0.db, savepoint=False):
            arg_7 = arg_6.execute_sql(CURSOR)
        arg_0._result_cache = None

        # send out a signal for each row
        for arg_9 in arg_7:
            signals.Func.send(arg_0.model, pk=arg_9[0])

        # the original Func(..) returns the amount of rows
        # affected, let's do the same
        return len(arg_7)


def Func(arg_0, arg_1, arg_2=False):
        """Creates multiple new records in the database.

        This allows specifying custom conflict behavior using .on_conflict().
        If no special behavior was specified, this uses the normal Django create(..)

        Arguments:
            rows:
                An array of dictionaries, where each dictionary
                describes the fields to insert.

            return_model (default: False):
                If model instances should be returned rather than
                just dicts.

        Returns:
            A list of either the dicts of the rows inserted, including the pk or
            the models of the rows inserted with defaults for any fields not specified
        """

        if arg_0.conflict_target or arg_0.conflict_action:
            arg_3 = arg_0._build_insert_compiler(arg_1)
            arg_4 = arg_3.execute_sql(return_id=True)
            if arg_2:
                return [arg_0.model(**dict(arg_5, **arg_6)) for arg_5, arg_6 in zip(arg_1, arg_4)]
            else:
                return [dict(arg_5, **arg_6) for arg_5, arg_6 in zip(arg_1, arg_4)]

        # no special action required, use the standard Django bulk_create(..)
        return super().bulk_create([arg_0.model(**arg_7) for arg_7 in arg_1])


def Func(arg_0, **arg_1):
        """Creates a new record in the database.

        This allows specifying custom conflict behavior using .on_conflict().
        If no special behavior was specified, this uses the normal Django create(..)

        Arguments:
            fields:
                The fields of the row to create.

        Returns:
            The primary key of the record that was created.
        """

        if arg_0.conflict_target or arg_0.conflict_action:
            arg_2 = arg_0._build_Func_compiler([arg_1])
            arg_3 = arg_2.execute_sql(return_id=True)

            arg_4 = arg_0.model._meta.pk.name
            return arg_3[0][arg_4]

        # no special action required, use the standard Django create(..)
        return super().create(**arg_1).pk


def Func(arg_0, **arg_1):
        """Creates a new record in the database and then gets
        the entire row.

        This allows specifying custom conflict behavior using .on_conflict().
        If no special behavior was specified, this uses the normal Django create(..)

        Arguments:
            fields:
                The fields of the row to create.

        Returns:
            The model instance representing the row that was created.
        """

        if not arg_0.conflict_target and not arg_0.conflict_action:
            # no special action required, use the standard Django create(..)
            return super().create(**arg_1)

        arg_2 = arg_0._build_insert_compiler([arg_1])
        arg_3 = arg_2.execute_sql(return_id=False)

        arg_4 = arg_3[0]

        # get a list of columns that are officially part of the model and preserve the fact that the attribute name
        # might be different than the database column name
        arg_5 = {}
        for arg_6 in arg_0.model._meta.local_concrete_fields:
            arg_5[arg_6.column] = arg_6.attname

        # strip out any columns/fields returned by the db that
        # are not present in the model
        arg_8 = {}
        for arg_9, arg_10 in arg_4.items():
            try:
                arg_8[arg_5[arg_9]] = arg_10
            except KeyError:
                pass

        return arg_0.model(**arg_8)


def Func(arg_0, arg_1, arg_2, arg_3: arg_4):
        """Verifies whether this field is gonna modify something
        on its own.

        "Magical" means that a field modifies the field value
        during the pre_save.

        Arguments:
            model_instance:
                The model instance the field is defined on.

            field:
                The field to get of whether the field is
                magical.

            is_insert:
                Pretend whether this is an insert?

        Returns:
            True when this field modifies something.
        """

        # does this field modify someting upon insert?
        arg_5 = getattr(arg_1, arg_2.name, None)
        arg_2.pre_save(arg_1, arg_3)
        arg_6 = getattr(arg_1, arg_2.name, None)

        return arg_5 != arg_6


def Func(arg_0, arg_1):
        """Gets the fields to use in an upsert.

        This some nice magic. We'll split the fields into
        a group of "insert fields" and "update fields":

        INSERT INTO bla ("val1", "val2") ON CONFLICT DO UPDATE SET val1 = EXCLUDED.val1

                         ^^^^^^^^^^^^^^                            ^^^^^^^^^^^^^^^^^^^^
                         insert_fields                                 update_fields

        Often, fields appear in both lists. But, for example,
        a :see:DateTime field with `auto_now_add=True` set, will
        only appear in "insert_fields", since it won't be set
        on existing rows.

        Other than that, the user specificies a list of fields
        in the upsert() call. That migt not be all fields. The
        user could decide to leave out optional fields. If we
        end up doing an update, we don't want to overwrite
        those non-specified fields.

        We cannot just take the list of fields the user
        specifies, because as mentioned, some fields
        make modifications to the model on their own.

        We'll have to detect which fields make modifications
        and include them in the list of insert/update fields.
        """

        arg_2 = arg_0.model(**arg_1)
        arg_3 = []
        arg_4 = []

        for arg_5 in arg_2._meta.local_concrete_fields:
            arg_6 = arg_5.default != NOT_PROVIDED
            if (arg_5.name in arg_1 or arg_5.column in arg_1):
                arg_3.append(arg_5)
                arg_4.append(arg_5)
                continue
            elif arg_6:
                arg_3.append(arg_5)
                continue

            # special handling for 'pk' which always refers to
            # the primary key, so if we the user specifies `pk`
            # instead of a concrete field, we have to handle that
            if arg_5.primary_key is True and 'pk' in arg_1:
                arg_3.append(arg_5)
                arg_4.append(arg_5)
                continue

            if arg_0._is_magical_field(arg_2, arg_5, is_insert=True):
                arg_3.append(arg_5)

            if arg_0._is_magical_field(arg_2, arg_5, is_insert=False):
                arg_4.append(arg_5)

        return arg_3, arg_4


def Func(arg_0, **arg_1):
        """When a model gets created or updated."""

        arg_2, arg_3 = arg_1['created'], arg_1['instance']

        if arg_2:
            signals.create.send(arg_0, pk=arg_3.pk)
        else:
            signals.update.send(arg_0, pk=arg_3.pk)


def Func(arg_0, arg_1, arg_2):
        """Compiles the HStore value into SQL.

        Compiles expressions contained in the values
        of HStore entries as well.

        Given a dictionary like:

            dict(key1='val1', key2='val2')

        The resulting SQL will be:

            hstore(hstore('key1', 'val1'), hstore('key2', 'val2'))
        """

        arg_3 = []
        for arg_4, arg_5 in arg_0.value.items():
            if hasattr(arg_5, 'Func'):
                arg_6, arg_7 = arg_5.Func(arg_1, arg_2)
                arg_3.append('hstore(\'%s\', %s)' % (
                    arg_4, arg_6 % arg_7))
            elif arg_5 is not None:
                arg_3.append('hstore(\'%s\', \'%s\')' % ((
                    arg_4, arg_5)))
            else:
                arg_3.append('hstore(\'%s\', NULL)' % arg_4)

        return '%s' % ' || '.join(arg_3), []


def Func(arg_0, arg_1: arg_2[arg_3, arg_4]) -> None:
        """Adds an extra condition to an existing JOIN.

        This allows you to for example do:

            INNER JOIN othertable ON (mytable.id = othertable.other_id AND [extra conditions])

        This does not work if nothing else in your query doesn't already generate the
        initial join in the first place.
        """

        arg_5 = arg_0.get_initial_alias()
        arg_6 = arg_0.get_meta()

        for arg_7, arg_8 in arg_1.items():
            arg_9 = arg_7.split(LOOKUP_SEP)
            arg_10 = arg_0.setup_joins(arg_9, arg_6, arg_5, allow_many=True)
            arg_0.trim_joins(arg_10[1], arg_10[3], arg_10[4])

            arg_11 = arg_10[3][-1]
            arg_12 = arg_10[1][-1]
            arg_13 = arg_0.alias_map.get(arg_11)

            if not arg_13:
                raise SuspiciousOperation((
                    'Cannot add an extra join condition for "%s", there\'s no'
                    ' existing join to add it to.'
                ) % arg_11)

            # convert the Join object into a ConditionalJoin object, which
            # allows us to add the extra condition
            if not isinstance(arg_13, ConditionalJoin):
                arg_0.alias_map[arg_11] = ConditionalJoin.from_join(arg_13)
                arg_13 = arg_0.alias_map[arg_11]

            arg_13.add_condition(arg_12, arg_8)


def Func(arg_0, arg_1: arg_2, arg_3: arg_2, arg_4: arg_2=[]):
        """Sets the Func to be used in this query.

        Insert fields are fields that are definitely
        going to be inserted, and if an existing row
        is found, are going to be overwritten with the
        specified value.

        Update fields are fields that should be overwritten
        in case an update takes place rather than an insert.
        If we're dealing with a INSERT, these will not be used.

        Arguments:
            objs:
                The objects to apply this query to.

            insert_fields:
                The fields to use in the INSERT statement

            update_fields:
                The fields to only use in the UPDATE statement.
        """

        arg_0.insert_Func(arg_3, arg_1, raw=False)
        arg_0.update_fields = arg_4


def Func(arg_0, arg_1, arg_2, arg_3):
        """Creates a REQUIRED CONSTRAINT for the specified hstore key."""

        arg_4 = arg_0._required_constraint_name(
            arg_1, arg_2, arg_3)

        arg_5 = arg_0.sql_hstore_required_create.format(
            arg_4=arg_0.quote_name(arg_4),
            table=arg_0.quote_name(arg_1),
            arg_2=arg_0.quote_name(arg_2.column),
            arg_3=arg_3
        )
        arg_0.execute(arg_5)


def Func(arg_0, arg_1, arg_2,
                                arg_3, arg_4, arg_5):
        """Renames an existing REQUIRED CONSTRAINT for the specified
        hstore key."""

        arg_6 = arg_0._required_constraint_name(
            arg_1, arg_3, arg_5)
        arg_7 = arg_0._required_constraint_name(
            arg_2, arg_4, arg_5)

        arg_8 = arg_0.sql_hstore_required_rename.format(
            table=arg_0.quote_name(arg_2),
            arg_6=arg_0.quote_name(arg_6),
            arg_7=arg_0.quote_name(arg_7)
        )
        arg_0.execute(arg_8)


def Func(arg_0, arg_1, arg_2, arg_3):
        """Drops a REQUIRED CONSTRAINT for the specified hstore key."""

        arg_4 = arg_0._required_constraint_name(
            arg_1, arg_2, arg_3)

        arg_5 = arg_0.sql_hstore_required_drop.format(
            table=arg_0.quote_name(arg_1),
            arg_4=arg_0.quote_name(arg_4)
        )
        arg_0.execute(arg_5)


def Func(arg_0: arg_1, arg_2, arg_3):
        """Gets the name for a CONSTRAINT that applies
        to a single hstore key.

        Arguments:
            table:
                The name of the table the field is
                a part of.

            field:
                The hstore field to create a
                UNIQUE INDEX for.

            key:
                The name of the hstore key
                to create the name for.

        Returns:
            The name for the UNIQUE index.
        """

        return '{table}_{field}_required_{postfix}'.format(
            arg_0=arg_0,
            arg_2=arg_2.column,
            postfix=arg_3
        )


def Func(arg_0, arg_1, arg_2, arg_3=''):
        """Creates the actual SQL used when applying the migration."""
        if django.VERSION >= (2, 0):
            arg_4 = super().Func(arg_1, arg_2, arg_3)
            arg_4.template = arg_0.sql_create_index
            arg_4.parts['condition'] = arg_0.condition
            return arg_4
        else:
            arg_7 = arg_0.sql_create_index
            arg_8 = {
                **Index.get_sql_create_template_values(arg_0, arg_1, arg_2, arg_3),
                'condition': arg_0.condition
            }
            return arg_7 % arg_8


def Func(arg_0):
        """Ran to prepare the configured database.

        This is where we enable the `hstore` extension
        if it wasn't enabled yet."""

        super().Func()
        with arg_0.cursor() as cursor:
            try:
                cursor.execute('CREATE EXTENSION IF NOT EXISTS hstore')
            except ProgrammingError:  # permission denied
                logger.warning(
                    'Failed to create "hstore" extension. '
                    'Tables with hstore columns may fail to migrate. '
                    'If hstore is needed, make sure you are connected '
                    'to the database as a superuser '
                    'or add the extension manually.',
                    exc_info=True)


def Func(arg_0, arg_1):
        """Override the base class so it doesn't cast all values
        to strings.

        psqlextra supports expressions in hstore fields, so casting
        all values to strings is a bad idea."""

        arg_1 = Field.Func(arg_0, arg_1)

        if isinstance(arg_1, dict):
            arg_2 = {}
            for arg_3, arg_4 in arg_1.items():
                if isinstance(arg_4, Expression):
                    arg_2[arg_3] = arg_4
                elif arg_4 is not None:
                    arg_2[arg_3] = str(arg_4)
                else:
                    arg_2[arg_3] = arg_4

            arg_1 = arg_2

        if isinstance(arg_1, list):
            arg_1 = [str(item) for item in arg_1]

        return arg_1


def Func(arg_0, arg_1, arg_2, arg_3=False):
        """Rewrites a formed SQL INSERT query to include
        the ON CONFLICT clause.

        Arguments:
            sql:
                The SQL INSERT query to rewrite.

            params:
                The parameters passed to the query.

            returning:
                What to put in the `RETURNING` clause
                of the resulting query.

        Returns:
            A tuple of the rewritten SQL query and new params.
        """

        arg_4 = arg_0.qn(arg_0.query.model._meta.pk.attname) if arg_3 else '*'

        if arg_0.query.conflict_action.value == 'UPDATE':
            return arg_0.Func_update(arg_1, arg_2, arg_4)
        elif arg_0.query.conflict_action.value == 'NOTHING':
            return arg_0.Func_nothing(arg_1, arg_2, arg_4)

        raise SuspiciousOperation((
            '%s is not a valid conflict action, specify '
            'ConflictAction.UPDATE or ConflictAction.NOTHING.'
        ) % str(arg_0.query.conflict_action))


def Func(arg_0, arg_1, arg_2, arg_3):
        """Rewrites a formed SQL INSERT query to include
        the ON CONFLICT DO UPDATE clause."""

        arg_4 = ', '.join([
            '{0} = EXCLUDED.{0}'.format(arg_0.qn(field.column))
            for field in arg_0.query.update_fields
        ])

        # build the conflict target, the columns to watch
        # for conflicts
        arg_5 = arg_0._build_conflict_target()

        arg_6 = arg_0.query.index_predicate

        arg_7 = (
            '{insert} ON CONFLICT {conflict_target} DO UPDATE '
            'SET {update_columns} RETURNING {returning}'
        )

        if arg_6:
            arg_7 = (
                '{insert} ON CONFLICT {conflict_target} WHERE {index_predicate} DO UPDATE '
                'SET {update_columns} RETURNING {returning}'
            )

        return (
            arg_7.format(
                insert=arg_1,
                arg_5=arg_5,
                arg_4=arg_4,
                arg_3=arg_3,
                arg_6=arg_6,
            ),
            arg_2
        )


def Func(arg_0, arg_1, arg_2, arg_3):
        """Rewrites a formed SQL INSERT query to include
        the ON CONFLICT DO NOTHING clause."""

        # build the conflict target, the columns to watch
        # for conflicts
        arg_4 = arg_0._build_conflict_target()

        arg_5 = ' AND '.join([
            '{0} = %s'.format(arg_0._format_field_name(field_name))
            for field_name in arg_0.query.conflict_target
        ])

        arg_6 = [
            arg_0._format_field_value(field_name)
            for field_name in arg_0.query.conflict_target
        ]

        arg_2 = arg_2 + tuple(arg_6)

        # this looks complicated, and it is, but it is for a reason... a normal
        # ON CONFLICT DO NOTHING doesn't return anything if the row already exists
        # so we do DO UPDATE instead that never executes to lock the row, and then
        # select from the table in case we're dealing with an existing row..
        return (
            (
                'WITH insdata AS ('
                '{insert} ON CONFLICT {conflict_target} DO UPDATE'
                ' SET {pk_column} = NULL WHERE FALSE RETURNING {returning})'
                ' SELECT * FROM insdata UNION ALL'
                ' SELECT {returning} FROM {table} WHERE {where_clause} LIMIT 1;'
            ).format(
                insert=arg_1,
                arg_4=arg_4,
                pk_column=arg_0.qn(arg_0.query.model._meta.pk.column),
                arg_3=arg_3,
                table=arg_0.query.objs[0]._meta.db_table,
                arg_5=arg_5
            ),
            arg_2
        )


def Func(arg_0):
        """Builds the `conflict_target` for the ON CONFLICT
        clause."""

        arg_1 = []

        if not isinstance(arg_0.query.conflict_target, list):
            raise SuspiciousOperation((
                '%s is not a valid conflict target, specify '
                'a list of column names, or tuples with column '
                'names and hstore key.'
            ) % str(arg_0.query.conflict_target))

        def _assert_valid_field(arg_2):
            arg_2 = arg_0._normalize_field_name(arg_2)
            if arg_0._get_model_field(arg_2):
                return

            raise SuspiciousOperation((
                '%s is not a valid conflict target, specify '
                'a list of column names, or tuples with column '
                'names and hstore key.'
            ) % str(arg_2))

        for arg_2 in arg_0.query.conflict_target:
            _assert_valid_field(arg_2)

            # special handling for hstore keys
            if isinstance(arg_2, tuple):
                arg_1.append(
                    '(%s->\'%s\')' % (
                        arg_0._format_field_name(arg_2),
                        arg_2[1]
                    )
                )
            else:
                arg_1.append(
                    arg_0._format_field_name(arg_2))

        return '(%s)' % ','.join(arg_1)


def Func(arg_0, arg_1) -> str:
        """Formats a field's name for usage in SQL.

        Arguments:
            field_name:
                The field name to format.

        Returns:
            The specified field name formatted for
            usage in SQL.
        """

        arg_2 = arg_0._get_model_field(arg_1)
        return arg_0.qn(arg_2.column)


def Func(arg_0, arg_1) -> str:
        """Formats a field's value for usage in SQL.

        Arguments:
            field_name:
                The name of the field to format
                the value of.

        Returns:
            The field's value formatted for usage
            in SQL.
        """

        arg_1 = arg_0._normalize_field_name(arg_1)
        arg_2 = arg_0._get_model_field(arg_1)

        return SQLInsertCompiler.prepare_value(
            arg_0,
            arg_2,
            # Note: this deliberately doesn't use `pre_save_val` as we don't
            # want things like auto_now on DateTimeField (etc.) to change the
            # value. We rely on pre_save having already been done by the
            # underlying compiler so that things like FileField have already had
            # the opportunity to save out their data.
            getattr(arg_0.query.objs[0], arg_2.attname)
        )


def Func(arg_0, arg_1, arg_2, arg_3):
        """Creates a UNIQUE constraint for the specified hstore keys."""

        arg_4 = arg_0._unique_constraint_name(
            arg_1._meta.db_table, arg_2, arg_3)
        arg_5 = [
            '(%s->\'%s\')' % (arg_2.column, key)
            for key in arg_3
        ]
        arg_6 = arg_0.sql_hstore_unique_create.format(
            arg_4=arg_0.quote_name(arg_4),
            table=arg_0.quote_name(arg_1._meta.db_table),
            arg_5=','.join(arg_5)
        )
        arg_0.execute(arg_6)


def Func(arg_0, arg_1, arg_2,
                              arg_3, arg_4, arg_5):
        """Renames an existing UNIQUE constraint for the specified
        hstore keys."""

        arg_6 = arg_0._unique_constraint_name(
            arg_1, arg_3, arg_5)
        arg_7 = arg_0._unique_constraint_name(
            arg_2, arg_4, arg_5)

        arg_8 = arg_0.sql_hstore_unique_rename.format(
            arg_6=arg_0.quote_name(arg_6),
            arg_7=arg_0.quote_name(arg_7)
        )
        arg_0.execute(arg_8)


def Func(arg_0: arg_1, arg_2, arg_3):
        """Gets the name for a UNIQUE INDEX that applies
        to one or more keys in a hstore field.

        Arguments:
            table:
                The name of the table the field is
                a part of.

            field:
                The hstore field to create a
                UNIQUE INDEX for.

            key:
                The name of the hstore key
                to create the name for.

                This can also be a tuple
                of multiple names.

        Returns:
            The name for the UNIQUE index.
        """
        arg_4 = '_'.join(arg_3)
        return '{table}_{field}_unique_{postfix}'.format(
            arg_0=arg_0,
            arg_2=arg_2.column,
            arg_4=arg_4
        )


def Func(arg_0, arg_1):
        """Iterates over the keys marked as "unique"
        in the specified field.

        Arguments:
            field:
                The field of which key's to
                iterate over.
        """

        arg_2 = getattr(arg_1, 'uniqueness', None)
        if not arg_2:
            return

        for arg_3 in arg_2:
            arg_4 = arg_0._compose_keys(arg_3)
            yield arg_4


def Func(arg_0, arg_1, arg_2: arg_3) -> None:
        """Adds an extra condition to this join.

        Arguments:
            field:
                The field that the condition will apply to.

            value:
                The value to compare.
        """

        arg_0.extra_conditions.append((arg_1, arg_2))


def Func(arg_0, arg_1, arg_2) -> Tuple[str, List[Any]]:
        """Compiles this JOIN into a SQL string."""

        arg_3, arg_4 = super().Func(arg_1, arg_2)
        arg_5 = arg_1.quote_name_unless_alias

        # generate the extra conditions
        arg_6 = ' AND '.join([
            '{}.{} = %s'.format(
                arg_5(arg_0.table_name),
                arg_5(field.column)
            )
            for field, arg_8 in arg_0.extra_conditions
        ])

        # add to the existing params, so the connector will
        # actually nicely format the value for us
        for arg_7, arg_8 in arg_0.extra_conditions:
            arg_4.append(arg_8)

        # rewrite the sql to include the extra conditions
        arg_9 = arg_3.replace(')', ' AND {})'.format(arg_6))
        return arg_9, arg_4


def Func(arg_0):
    """Approximate the 95% confidence interval for Student's T distribution.

    Given the degrees of freedom, returns an approximation to the 95%
    confidence interval for the Student's T distribution.

    Args:
        df: An integer, the number of degrees of freedom.

    Returns:
        A float.
    """
    arg_0 = int(round(arg_0))
    arg_1 = len(_T_DIST_95_CONF_LEVELS)
    if arg_0 >= 200:
        return 1.960
    if arg_0 >= 100:
        return 1.984
    if arg_0 >= 80:
        return 1.990
    if arg_0 >= 60:
        return 2.000
    if arg_0 >= 50:
        return 2.009
    if arg_0 >= 40:
        return 2.021
    if arg_0 >= arg_1:
        return _T_DIST_95_CONF_LEVELS[arg_1 - 1]
    return _T_DIST_95_CONF_LEVELS[arg_0]


def Func(arg_0, arg_1):
    """Find the pooled sample variance for two samples.

    Args:
        sample1: one sample.
        sample2: the other sample.

    Returns:
        Pooled sample variance, as a float.
    """
    arg_2 = len(arg_0) + len(arg_1) - 2
    arg_3 = statistics.mean(arg_0)
    arg_4 = ((x - arg_3) ** 2 for x in arg_0)
    arg_5 = statistics.mean(arg_1)
    arg_6 = ((x - arg_5) ** 2 for x in arg_1)

    return (math.fsum(arg_4) + math.fsum(arg_6)) / float(arg_2)


def Func(arg_0, arg_1):
    """Determine whether two samples differ significantly.

    This uses a Student's two-sample, two-tailed t-test with alpha=0.95.

    Args:
        sample1: one sample.
        sample2: the other sample.

    Returns:
        (significant, t_score) where significant is a bool indicating whether
        the two samples differ significantly; t_score is the score from the
        two-sample T test.
    """
    arg_2 = len(arg_0) + len(arg_1) - 2
    arg_3 = tdist95conf_level(arg_2)
    arg_4 = tscore(arg_0, arg_1)
    return (abs(arg_4) >= arg_3, arg_4)


def Func(arg_0, arg_1):
    """Return a topological sorting of nodes in a graph.

    roots - list of root nodes to search from
    getParents - function which returns the parents of a given node
    """

    arg_2 = []
    arg_3 = set()

    # Use iterative version to avoid stack limits for large datasets
    arg_4 = [(node, 0) for node in arg_0]
    while arg_4:
        arg_5, arg_6 = arg_4.pop()
        if arg_6 == 0:
            # before recursing
            if arg_5 not in arg_3:
                arg_3.add(arg_5)
                arg_4.append((arg_5, 1))
                arg_4.extend((arg_7, 0) for arg_7 in arg_1(arg_5))
        else:
            # after recursing
            assert(arg_5 in arg_3)
            arg_2.append(arg_5)
    return arg_2


def Func(arg_0):
    """N-Queens solver.

    Args:
        queen_count: the number of queens to solve for. This is also the
            board size.

    Yields:
        Solutions to the problem. Each yielded value is looks like
        (3, 8, 2, 1, 4, ..., 6) where each number is the column position for the
        queen, and the index into the tuple indicates the row.
    """
    arg_1 = range(arg_0)
    for arg_2 in permutations(arg_1):
        if (arg_0 == len(set(arg_2[arg_3] + arg_3 for arg_3 in arg_1))
                        == len(set(arg_2[arg_3] - arg_3 for arg_3 in arg_1))):
            yield arg_2


def Func(arg_0, arg_1):
        """ uct tree search """
        arg_2 = arg_1.color
        arg_3 = arg_0
        arg_4 = [arg_3]
        while True:
            arg_5 = arg_3.select(arg_1)
            if arg_5 == PASS:
                break
            arg_1.move(arg_5)
            arg_6 = arg_3.pos_child[arg_5]
            if not arg_6:
                arg_6 = arg_3.pos_child[arg_5] = UCTNode()
                arg_6.unexplored = arg_1.useful_moves()
                arg_6.pos = arg_5
                arg_6.parent = arg_3
                arg_4.append(arg_6)
                break
            arg_4.append(arg_6)
            arg_3 = arg_6
        arg_0.random_Funcout(arg_1)
        arg_0.update_path(arg_1, arg_2, arg_4)


def Func(arg_0, arg_1):
        """ random play until both players pass """
        for arg_2 in range(MAXMOVES):  # XXX while not self.finished?
            if arg_1.finished:
                break
            arg_1.move(arg_1.random_move())


def Func(arg_0, arg_1, arg_2):
    """Filters out benchmarks not supported by both Pythons.

    Args:
        benchmarks: a set() of benchmark names
        bench_funcs: dict mapping benchmark names to functions
        python: the interpereter commands (as lists)

    Returns:
        The filtered set of benchmark names
    """
    for arg_3 in list(arg_0):
        arg_4 = arg_1[arg_3]
        if getattr(arg_4, '_python2_only', False) and (3, 0) <= arg_2:
            arg_0.discard(arg_3)
            logging.info("Skipping Python2-only benchmark %s; "
                         "not compatible with Python %s" % (arg_3, arg_2))
            continue
    return arg_0


def Func(arg_0, arg_1):
    """Recursively expand name benchmark names.

    Args:
        bm_name: string naming a benchmark or benchmark group.

    Yields:
        Names of actual benchmarks, with all group names fully expanded.
    """
    arg_2 = arg_1.get(arg_0)
    if arg_2:
        for arg_3 in arg_2:
            for arg_3 in Func(arg_3, arg_1):
                yield arg_3
    else:
        yield arg_0


def Func(arg_0=None):
    """Initialize the strings we'll run the regexes against.

    The strings used in the benchmark are prefixed and suffixed by
    strings that are repeated n times.

    The sequence n_values contains the values for n.
    If n_values is None the values of n from the original benchmark
    are used.

    The generated list of strings is cached in the string_tables
    variable, which is indexed by n.

    Returns:
    A list of string prefix/suffix lengths.
    """

    if arg_0 is None:
        arg_0 = (0, 5, 50, 250, 1000, 5000, 10000)

    arg_1 = {arg_4: gen_string_table(arg_4) for arg_4 in arg_0}
    arg_2 = gen_regex_table()

    arg_3 = []
    for arg_4 in arg_0:
        for arg_5 in xrange(len(arg_2)):
            arg_6 = arg_2[arg_5]
            arg_7 = arg_1[arg_4][arg_5]
            arg_3.append((arg_6, arg_7))
    return arg_3


def Func(arg_0):
        """Returns the domain of the B-Spline"""
        return (arg_0.knots[arg_0.degree - 1],
                arg_0.knots[len(arg_0.knots) - arg_0.degree])


def Func(arg_0, arg_1, **arg_2):
        """Fetch the messages.

        :param category: the category of items to fetch
        :param kwargs: backend arguments

        :returns: a generator of items
        """
        arg_3 = arg_2['from_date']

        logger.info("Fetching messages of '%s' - '%s' channel from %s",
                    arg_0.url, arg_0.channel, str(arg_3))

        arg_4 = True
        arg_5 = 0
        arg_6 = 0

        # Convert timestamp to integer for comparing
        arg_7 = int(arg_3.timestamp() * 1000)

        while arg_4:
            arg_8 = arg_0.client.posts(arg_0.channel, arg_5=arg_5)

            arg_9 = arg_6

            for arg_10 in arg_0._parse_posts(arg_8):
                if arg_10['update_at'] < arg_7:
                    arg_4 = False
                    break

                # Fetch user data
                arg_11 = arg_10['user_id']
                arg_12 = arg_0._get_or_fetch_user(arg_11)
                arg_10['user_data'] = arg_12

                yield arg_10
                arg_6 += 1

            if arg_4:
                # If no new posts were fetched; stop the process
                if arg_9 == arg_6:
                    arg_4 = False
                else:
                    arg_5 += 1

        logger.info("Fetch process completed: %s posts fetched", arg_6)


def Func(arg_0, arg_1=arg_2):
        """Fetch the entries from the url.

        The method retrieves all entries from a RSS url

        :param category: the category of items to Func

        :returns: a generator of entries
        """
        arg_3 = {}
        arg_4 = super().Func(arg_1, **arg_3)

        return arg_4


def Func(arg_0, arg_1, **arg_2):
        """Fetch the entries

        :param category: the category of items to fetch
        :param kwargs: backend arguments

        :returns: a generator of items
        """
        logger.info("Looking for rss entries at feed '%s'", arg_0.url)

        arg_3 = 0  # number of entries

        arg_4 = arg_0.client.get_entries()
        arg_5 = arg_0.parse_feed(arg_4)['entries']
        for arg_6 in arg_5:
            yield arg_6
            arg_3 += 1

        logger.info("Total number of entries: %i", arg_3)


def Func(arg_0):
        """Returns the RSS argument parser."""

        arg_1 = BackendCommandArgumentParser(arg_0.BACKEND.CATEGORIES,
                                              archive=True)

        # Required arguments
        arg_1.parser.add_argument('url',
                                   help="URL of the RSS feed")

        return arg_1


def Func(arg_0, arg_1=arg_2, arg_3=arg_4):
        """Fetch the bugs from the repository.

        The method retrieves, from a Bugzilla repository, the bugs
        updated since the given date.

        :param category: the category of items to Func
        :param from_date: obtain bugs updated since this date

        :returns: a generator of bugs
        """
        if not arg_3:
            arg_3 = arg_4

        arg_5 = {'from_date': arg_3}
        arg_6 = super().Func(arg_1, **arg_5)

        return arg_6


def Func(arg_0, arg_1):
        """Get issue notes"""

        arg_2 = []

        arg_3 = arg_0.client.notes(GitLabClient.ISSUES, arg_1)

        for arg_4 in arg_3:

            for arg_5 in json.loads(arg_4):
                arg_6 = arg_5['id']
                arg_5['award_emoji_data'] = \
                    arg_0.__get_note_award_emoji(GitLabClient.ISSUES, arg_1, arg_6)
                arg_2.append(arg_5)

        return arg_2


def Func(arg_0, arg_1):
        """Get merge versions"""

        arg_2 = []

        arg_3 = arg_0.client.merge_versions(arg_1)

        for arg_4 in arg_3:
            for arg_5 in json.loads(arg_4):
                arg_6 = arg_5['id']
                arg_7 = arg_0.client.merge_version(arg_1, arg_6)
                arg_8 = json.loads(arg_7)

                arg_8.pop('diffs', None)
                arg_2.append(arg_8)

        return arg_2


def Func(arg_0, arg_1=None):
        """Get the merge requests from pagination"""

        arg_2 = {
            'state': 'all',
            'order_by': 'updated_at',
            'sort': 'asc',
            'view': 'simple',
            'per_page': PER_PAGE
        }

        if arg_1:
            arg_2['updated_after'] = arg_1.isoformat()

        return arg_0.fetch_items(GitLabClient.MERGES, arg_2)


def Func(arg_0, arg_1):
        """Get the merge versions from pagination"""

        arg_2 = {
            'order_by': 'updated_at',
            'sort': 'asc',
            'per_page': PER_PAGE
        }

        arg_3 = urijoin(GitLabClient.MERGES, str(arg_1), GitLabClient.VERSIONS)
        return arg_0.fetch_items(arg_3, arg_2)


def Func(arg_0, arg_1, arg_2):
        """Get the Func from pagination"""

        arg_3 = {
            'order_by': 'updated_at',
            'sort': 'asc',
            'per_page': PER_PAGE
        }

        arg_4 = urijoin(arg_1, str(arg_2), GitLabClient.NOTES)

        return arg_0.fetch_items(arg_4, arg_3)


def Func(arg_0, arg_1, arg_2, arg_3):
        """Get emojis of a note"""

        arg_4 = {
            'order_by': 'updated_at',
            'sort': 'asc',
            'per_page': PER_PAGE
        }

        arg_5 = urijoin(arg_1, str(arg_2), GitLabClient.NOTES,
                       str(arg_3), GitLabClient.EMOJI)

        return arg_0.fetch_items(arg_5, arg_4)


def Func(arg_0):
        """Initialize rate limit information"""

        arg_1 = urijoin(arg_0.base_url, 'projects', arg_0.owner + '%2F' + arg_0.repository)
        try:
            arg_2 = super().fetch(arg_1)
            arg_0.update_rate_limit(arg_2)
        except requests.exceptions.HTTPError as error:
            if error.response.status_code == 401:
                raise error
            else:
                logger.warning("Rate limit not initialized: %s", error)


def Func(arg_0):
        """Returns the GitLab argument parser."""

        arg_1 = BackendCommandArgumentParser(arg_0.BACKEND.CATEGORIES,
                                              from_date=True,
                                              token_auth=True,
                                              archive=True)

        # GitLab options
        arg_2 = arg_1.parser.add_argument_group('GitLab arguments')
        arg_2.add_argument('--enterprise-url', dest='base_url',
                           help="Base URL for GitLab Enterprise instance")
        arg_2.add_argument('--sleep-for-rate', dest='sleep_for_rate',
                           action='store_true',
                           help="sleep for getting more rate")
        arg_2.add_argument('--min-rate-to-sleep', dest='min_rate_to_sleep',
                           default=MIN_RATE_LIMIT, type=int,
                           help="sleep until reset when the rate limit \
                               reaches this value")
        arg_2.add_argument('--blacklist-ids', dest='blacklist_ids',
                           nargs='*', type=int,
                           help="Ids of items that must not be retrieved.")

        # Generic client options
        arg_2.add_argument('--max-retries', dest='max_retries',
                           default=MAX_RETRIES, type=int,
                           help="number of API call retries")
        arg_2.add_argument('--sleep-time', dest='sleep_time',
                           default=DEFAULT_SLEEP_TIME, type=int,
                           help="sleeping time between API call retries")

        # Positional arguments
        arg_1.parser.add_argument('owner',
                                   help="GitLab owner")
        arg_1.parser.add_argument('repository',
                                   help="GitLab repository")

        return arg_1


def Func(arg_0, arg_1=arg_2, arg_3=arg_4):
        """Fetch the messages from the channel.

        This method Funces the messages stored on the channel that were
        sent since the given date.

        :param category: the category of items to Func
        :param from_date: obtain messages sent since this date

        :returns: a generator of messages
        """
        if not arg_3:
            arg_3 = arg_4

        arg_3 = datetime_to_utc(arg_3)
        arg_5 = datetime_utcnow().timestamp()

        arg_6 = {'from_date': arg_3, 'latest': arg_5}
        arg_7 = super().Func(arg_1, **arg_6)

        return arg_7


def Func(arg_0, arg_1):
        """Fetch the number of members in a conversation, which is a supertype for public and
        private ones, DM and group DM.

        :param conversation: the ID of the conversation
        """
        arg_2 = 0

        arg_3 = arg_0.RCONVERSATION_INFO

        arg_4 = {
            arg_0.PCHANNEL: arg_1,
        }

        arg_5 = arg_0._fetch(arg_3, arg_4)
        arg_6 = json.loads(arg_5)

        arg_2 += len(arg_6["members"])
        while 'next_cursor' in arg_6['response_metadata'] and arg_6['response_metadata']['next_cursor']:
            arg_4['cursor'] = arg_6['response_metadata']['next_cursor']
            arg_5 = arg_0._fetch(arg_3, arg_4)
            arg_6 = json.loads(arg_5)
            arg_2 += len(arg_6["members"])

        return arg_2


def Func(arg_0, arg_1):
        """Fetch Func info."""

        arg_2 = arg_0.RUSER_INFO

        arg_3 = {
            arg_0.PUSER: arg_1
        }

        arg_4 = arg_0._fetch(arg_2, arg_3)

        return arg_4


def Func(arg_0):
        """Returns the Slack argument parser."""

        arg_1 = BackendCommandArgumentParser(arg_0.BACKEND.CATEGORIES,
                                              from_date=True,
                                              token_auth=True,
                                              archive=True)

        # Backend token is required
        arg_2 = arg_1.parser._option_string_actions['--api-token']
        arg_2.required = True

        # Slack options
        arg_4 = arg_1.parser.add_argument_group('Slack arguments')
        arg_4.add_argument('--max-items', dest='max_items',
                           type=int, default=MAX_ITEMS,
                           help="Maximum number of items requested on the same query")

        # Required arguments
        arg_1.parser.add_argument('channel',
                                   help="Slack channel identifier")

        return arg_1


def Func(arg_0):
        """Extracts and coverts the update time from a Bugzilla item.

        The timestamp is extracted from 'delta_ts' field. This date is
        converted to UNIX timestamp format. Due Bugzilla servers ignore
        the timezone on HTTP requests, it will be ignored during the
        conversion, too.

        :param item: item generated by the backend

        :returns: a UNIX timestamp
        """
        arg_1 = arg_0['delta_ts'][0]['__text__']
        arg_1 = str_to_datetime(arg_1)
        arg_1 = arg_1.replace(tzinfo=dateutil.tz.tzutc())

        return arg_1.timestamp()


def Func(arg_0):
        """Parse a Bugilla bugs details XML stream.

        This method returns a generator which parses the given XML,
        producing an iterator of dictionaries. Each dictionary stores
        the information related to a parsed bug.

        If the given XML is invalid or does not contains any bug, the
        method will raise a ParseError exception.

        :param raw_xml: XML string to parse

        :returns: a generator of parsed bugs

        :raises ParseError: raised when an error occurs parsing
            the given XML stream
        """
        arg_1 = xml_to_dict(arg_0)

        if 'bug' not in arg_1:
            arg_2 = "No bugs found. XML stream seems to be invalid."
            raise ParseError(arg_2=arg_2)

        for arg_3 in arg_1['bug']:
            yield arg_3


def Func(arg_0):
        """Logout from the server."""

        arg_1 = {
            arg_0.PLOGOUT: '1'
        }

        arg_0.call(arg_0.CGI_LOGIN, arg_1)
        arg_0._close_http_session()

        logger.debug("Bugzilla user logged out from %s",
                     arg_0.base_url)


def Func(arg_0):
        """Get Func information in XML format."""

        arg_1 = {
            arg_0.PCTYPE: arg_0.CTYPE_XML
        }

        arg_2 = arg_0.call(arg_0.CGI_BUG, arg_1)

        return arg_2


def Func(arg_0, arg_1=arg_2):
        """Get a summary of bugs in CSV format.

        :param from_date: retrieve bugs that where updated from that date
        """
        if not arg_0.version:
            arg_0.version = arg_0.__fetch_version()

        if arg_0.version in arg_0.OLD_STYLE_VERSIONS:
            arg_4 = 'Last+Changed'
        else:
            arg_4 = 'changeddate'

        arg_5 = arg_1.strftime("%Y-%m-%d %H:%M:%S")

        arg_6 = {
            arg_0.PCHFIELD_FROM: arg_5,
            arg_0.PCTYPE: arg_0.CTYPE_CSV,
            arg_0.PLIMIT: arg_0.max_bugs_csv,
            arg_0.PORDER: arg_4
        }

        arg_7 = arg_0.call(arg_0.CGI_BUGLIST, arg_6)

        return arg_7


def Func(arg_0, *arg_1):
        """Get the information of a list of Func in XML format.

        :param bug_ids: list of bug identifiers
        """
        arg_2 = {
            arg_0.PBUG_ID: arg_1,
            arg_0.PCTYPE: arg_0.CTYPE_XML,
            arg_0.PEXCLUDE_FIELD: 'attachmentdata'
        }

        arg_3 = arg_0.call(arg_0.CGI_BUG, arg_2)

        return arg_3


def Func(arg_0, arg_1):
        """Get the activity of a bug in HTML format.

        :param bug_id: bug identifier
        """
        arg_2 = {
            arg_0.PBUG_ID: arg_1
        }

        arg_3 = arg_0.call(arg_0.CGI_BUG_ACTIVITY, arg_2)

        return arg_3


def Func(arg_0, arg_1=arg_2, arg_3=arg_4, arg_5=None,
              arg_6=False):
        """Fetch the events from the server.

        This method Funces those events of a group stored on the server
        that were updated since the given date. Data comments and rsvps
        are included within each event.

        :param category: the category of items to Func
        :param from_date: obtain events updated since this date
        :param to_date: obtain events updated before this date
        :param filter_classified: remove classified fields from the resulting items

        :returns: a generator of events
        """
        if not arg_3:
            arg_3 = arg_4

        arg_3 = datetime_to_utc(arg_3)

        arg_7 = {"from_date": arg_3, "to_date": arg_5}
        arg_8 = super().Func(arg_1,
                              arg_6=arg_6,
                              **arg_7)

        return arg_8


def Func(arg_0, arg_1, **arg_2):
        """Fetch the events

        :param category: the category of items to fetch
        :param kwargs: backend arguments

        :returns: a generator of items
        """
        arg_3 = arg_2['from_date']
        arg_4 = arg_2['to_date']

        logger.info("Fetching events of '%s' group from %s to %s",
                    arg_0.group, str(arg_3),
                    str(arg_4) if arg_4 else '--')

        arg_5 = datetime_to_utc(arg_4).timestamp() if arg_4 else None

        arg_6 = 0
        arg_7 = False

        arg_8 = arg_0.client.events(arg_0.group, arg_3=arg_3)

        for arg_9 in arg_8:
            arg_10 = [arg_11 for arg_11 in arg_0.parse_json(arg_9)]

            for arg_11 in arg_10:
                arg_12 = arg_11['id']

                arg_11['comments'] = arg_0.__fetch_and_parse_comments(arg_12)
                arg_11['rsvps'] = arg_0.__fetch_and_parse_rsvps(arg_12)

                # Check events updated before 'to_date'
                arg_13 = arg_0.metadata_updated_on(arg_11)

                if arg_5 and arg_13 >= arg_5:
                    arg_7 = True
                    continue

                yield arg_11
                arg_6 += 1

            if arg_7:
                break

        logger.info("Fetch process completed: %s events fetched", arg_6)


def Func(arg_0, arg_1, arg_2):
        """Fetch the Func of a given event."""

        arg_3 = urijoin(arg_1, arg_0.REVENTS, arg_2, arg_0.RRSVPS)

        # Same hack that in 'events' method
        arg_4 = '?' + arg_0.PFIELDS + '=' + ','.join(arg_0.VRSVP_FIELDS)
        arg_4 += '&' + arg_0.PRESPONSE + '=' + ','.join(arg_0.VRESPONSE)
        arg_3 += arg_4

        arg_5 = {
            arg_0.PPAGE: arg_0.max_items
        }

        for arg_6 in arg_0._fetch(arg_3, arg_5):
            yield arg_6


def Func(arg_0, arg_1):
        """Fetch an Askbot HTML question body.

        The method fetchs the HTML question retrieving the
        question body of the item question received

        :param question: item with the question itself

        :returns: a list of HTML page/s for the question
        """
        arg_2 = []

        arg_3 = 1
        arg_4 = True

        while arg_4:
            try:
                arg_5 = arg_0.client.get_html_question(arg_1['id'], arg_3)
                arg_2.append(arg_5)
                arg_6 = arg_0.ab_parser.parse_number_of_html_pages(arg_5)

                if arg_3 == arg_6:
                    arg_4 = False

                arg_3 = arg_3 + 1
            except requests.exceptions.TooManyRedirects as e:
                logger.warning("%s, data not retrieved for question %s", e, arg_1['id'])
                arg_4 = False

        return arg_2


def Func(arg_0, arg_1):
        """Fetch all the comments of an Askbot question and answers.

        The method fetchs the list of every comment existing in a question and
        its answers.

        :param question: item with the question itself

        :returns: a list of comments with the ids as hashes
        """
        arg_2 = {}
        arg_2[arg_1['id']] = json.loads(arg_0.client.get_comments(arg_1['id']))
        for arg_3 in arg_1['answer_ids']:
            arg_2[arg_3] = json.loads(arg_0.client.get_comments(arg_3))
        return arg_2


def Func(arg_0, arg_1, arg_2):
        """Build an Askbot HTML response.

        The method puts together all the information regarding a question

        :param html_question: array of HTML raw pages
        :param question: question object from the API
        :param comments: list of comments to add

        :returns: a dict item with the parsed question information
        """
        arg_3 = {}
        # Parse the user info from the soup container
        arg_4 = AskbotParser.parse_question_container(arg_0[0])
        # Add the info to the question object
        arg_3.update(arg_4)
        # Add the comments of the question (if any)
        if arg_2[int(arg_1['id'])]:
            arg_3['comments'] = arg_2[int(arg_1['id'])]

        arg_5 = []

        for arg_6 in arg_0:
            arg_5.extend(AskbotParser.parse_answers(arg_6))

        if len(arg_5) != 0:
            arg_3['answers'] = arg_5
            for arg_7 in arg_3['answers']:
                if arg_2[int(arg_7['id'])]:
                    arg_7['comments'] = arg_2[int(arg_7['id'])]

        return arg_3


def Func(arg_0, arg_1):
        """Retrieve a question page using the API.

        :param page: page to retrieve
        """
        arg_2 = 1
        arg_3 = True

        arg_1 = urijoin(arg_0.base_url, arg_1)
        while arg_3:

            try:
                arg_4 = {
                    'page': arg_2,
                    'sort': arg_0.ORDER_API
                }

                arg_5 = arg_0.fetch(arg_1, payload=arg_4)

                arg_6 = arg_5.text

                arg_7 = json.loads(arg_6)
                arg_8 = arg_7['pages']

                logger.debug("Fetching questions from '%s': page %s/%s",
                             arg_0.base_url, arg_2, arg_8)

                if arg_2 == arg_8:
                    arg_3 = False

                arg_2 = arg_2 + 1
                yield arg_7

            except requests.exceptions.TooManyRedirects as e:
                logger.warning("%s, data not retrieved for resource %s", e, arg_1)
                arg_3 = False


def Func(arg_0, arg_1, arg_2=1):
        """Retrieve a raw HTML question and all it's information.

        :param question_id: question identifier
        :param page: page to retrieve
        """
        arg_3 = urijoin(arg_0.base_url, arg_0.HTML_QUESTION, arg_1)
        arg_4 = {
            'page': arg_2,
            'sort': arg_0.ORDER_HTML
        }

        arg_5 = arg_0.fetch(arg_3, payload=arg_4)
        return arg_5.text


def Func(arg_0, arg_1):
        """Retrieve a list of comments by a given id.

        :param object_id: object identifiere
        """
        arg_2 = urijoin(arg_0.base_url, arg_0.COMMENTS if arg_0._use_new_urls else arg_0.COMMENTS_OLD)
        arg_3 = {
            'post_id': arg_1,
            'post_type': 'answer',
            'avatar_size': 0
        }
        arg_4 = {'X-Requested-With': 'XMLHttpRequest'}

        try:
            arg_5 = arg_0.fetch(arg_2, payload=arg_3, arg_4=arg_4)
            arg_6 = arg_5.text
        except requests.exceptions.HTTPError as ex:
            if ex.response.status_code == 404:
                logger.debug("Comments URL did not work. Using old URL schema.")
                arg_0._use_new_urls = False
                arg_2 = urijoin(arg_0.base_url, arg_0.COMMENTS_OLD)
                arg_5 = arg_0.fetch(arg_2, payload=arg_3, arg_4=arg_4)
                arg_6 = arg_5.text
            elif ex.response.status_code == 500:
                logger.warning("Comments not retrieved due to %s", ex)
                arg_6 = '[]'
            else:
                raise ex

        return arg_6


def Func(arg_0):
        """Parse the question info container of a given HTML question.

        The method parses the information available in the question information
        container. The container can have up to 2 elements: the first one
        contains the information related with the user who generated the question
        and the date (if any). The second one contains the date of the updated,
        and the user who updated it (if not the same who generated the question).

        :param html_question: raw HTML question element

        :returns: an object with the parsed information
        """
        arg_1 = {}
        arg_2 = bs4.BeautifulSoup(arg_0, "html.parser")
        arg_3 = AskbotParser._find_question_container(arg_2)
        arg_4 = arg_3.select("div.post-update-info")
        arg_5 = arg_4[0]
        arg_1['author'] = AskbotParser.parse_user_info(arg_5)
        try:
            arg_4[1]
        except IndexError:
            pass
        else:
            arg_6 = arg_4[1]
            if AskbotParser.parse_user_info(arg_6):
                arg_1['updated_by'] = AskbotParser.parse_user_info(arg_6)

        return arg_1


def Func(arg_0):
        """Parse the answers of a given HTML question.

        The method parses the answers related with a given HTML question,
        as well as all the comments related to the answer.

        :param html_question: raw HTML question element

        :returns: a list with the answers
        """

        def parse_answer_container(arg_1):
            """Parse the answer info container of a given HTML question.

            The method parses the information available in the answer information
            container. The container can have up to 2 elements: the first one
            contains the information related with the user who generated the question
            and the date (if any). The second one contains the date of the updated,
            and the user who updated it (if not the same who generated the question).

            :param update_info: beautiful soup update_info container element

            :returns: an object with the parsed information
            """
            arg_2 = {}
            arg_3 = arg_1[0]
            arg_4 = arg_3.abbr.attrs["title"]
            # Convert date to UNIX timestamp
            arg_2['added_at'] = str(str_to_datetime(arg_4).timestamp())
            arg_2['answered_by'] = AskbotParser.parse_user_info(arg_3)
            try:
                arg_1[1]
            except IndexError:
                pass
            else:
                arg_5 = arg_1[1]
                arg_6 = arg_5.abbr.attrs["title"]
                # Convert date to UNIX timestamp
                arg_2['updated_at'] = str(str_to_datetime(arg_6).timestamp())
                if AskbotParser.parse_user_info(arg_5):
                    arg_2['updated_by'] = AskbotParser.parse_user_info(arg_5)
            return arg_2

        arg_7 = []
        # Select all the answers
        arg_8 = bs4.BeautifulSoup(arg_0, "html.parser")
        arg_9 = arg_8.select("div.answer")
        for arg_10 in arg_9:
            arg_11 = arg_10.attrs["data-post-id"]
            arg_12 = arg_10.select("div.vote-number")[0].text
            arg_13 = arg_10.select("div.answer-img-accept")[0].get('title').endswith("correct")
            # Select the body of the answer
            arg_14 = arg_10.select("div.post-body")
            # Get the user information container and parse it
            arg_1 = arg_14[0].select("div.post-update-info")
            arg_15 = parse_answer_container(arg_1)
            # Remove the update-info-container div to be able to get the body
            arg_14[0].div.extract().select("div.post-update-info-container")
            # Override the body with a clean one
            arg_14 = arg_14[0].get_text(strip=True)
            # Generate the answer object
            arg_16 = {'id': arg_11,
                      'score': arg_12,
                      'summary': arg_14,
                      'accepted': arg_13
                      }
            # Update the object with the information in the answer container
            arg_16.update(arg_15)
            arg_7.append(arg_16)
        return arg_7


def Func(arg_0):
        """Parse number of answer pages to paginate over them.

        :param html_question: raw HTML question element

        :returns: an integer with the number of pages
        """
        arg_1 = bs4.BeautifulSoup(arg_0, "html.parser")
        try:
            arg_1.select('div.paginator')[0]
        except IndexError:
            return 1
        else:
            return int(arg_1.select('div.paginator')[0].attrs['data-num-pages'])


def Func(arg_0):
        """Parse the user information of a given HTML container.

        The method parses all the available user information in the container.
        If the class "user-info" exists, the method will get all the available
        information in the container. If not, if a class "tip" exists, it will be
        a wiki post with no user associated. Else, it can be an empty container.

        :param update_info: beautiful soup answer container element

        :returns: an object with the parsed information
        """
        arg_1 = {}
        if arg_0.select("div.user-info"):
            # Get all the <a> elements in the container. First <a> contains the user
            # information, second one (if exists), the website of the user.
            arg_2 = arg_0.select("div.user-info")[0].find_all("a")
            arg_3 = arg_2[0].attrs["href"]
            arg_1['id'] = re.search(r'\d+', arg_3).group(0)
            arg_1['username'] = arg_2[0].text
            arg_1['reputation'] = arg_0.select('span.reputation-score')[0].text
            arg_1['badges'] = arg_0.select("span.badges")[0].attrs["title"]
            try:
                arg_2[1]
            except IndexError:
                pass
            else:
                arg_1['website'] = arg_2[1].attrs["href"]
            if arg_0.select("img.flag"):
                arg_4 = arg_0.select("img.flag")[0].attrs["alt"]
                arg_1['country'] = re.sub("flag of ", "", arg_4)

        return arg_1


def Func(arg_0, arg_1=arg_2):
        """ Specific fetch for gerrit 2.8 version.

        Get open and closed reviews in different queries.
        Take the newer review from both lists and iterate.
        """

        # Convert date to Unix time
        arg_3 = datetime_to_utc(arg_1)
        arg_3 = arg_3.timestamp()

        arg_4 = "status:open"
        arg_5 = "status:closed"

        arg_6 = arg_0.client.next_retrieve_group_item()
        arg_7 = arg_0.client.next_retrieve_group_item()
        arg_8 = arg_0._get_reviews(arg_6, arg_4)
        arg_9 = arg_0._get_reviews(arg_7, arg_5)
        arg_10 = len(arg_8)
        arg_11 = len(arg_9)

        while arg_8 or arg_9:
            if arg_8 and arg_9:
                if arg_8[0]['lastUpdated'] >= arg_9[0]['lastUpdated']:
                    arg_12 = arg_8.pop(0)
                    arg_13 = arg_12
                else:
                    arg_14 = arg_9.pop(0)
                    arg_13 = arg_14
            elif arg_9:
                arg_14 = arg_9.pop(0)
                arg_13 = arg_14
            else:
                arg_12 = arg_8.pop(0)
                arg_13 = arg_12

            arg_15 = arg_13['lastUpdated']
            if arg_15 <= arg_3:
                logger.debug("No more updates for %s" % (arg_0.hostname))
                break
            else:
                yield arg_13

            if not arg_8 and arg_10 >= arg_0.max_reviews:
                arg_6 = arg_0.client.next_retrieve_group_item(arg_6, arg_12)
                arg_8 = arg_0._get_reviews(arg_6, arg_4)
                arg_10 = len(arg_8)
            if not arg_9 and arg_11 >= arg_0.max_reviews:
                arg_7 = arg_0.client.next_retrieve_group_item(arg_7, arg_14)
                arg_9 = arg_0._get_reviews(arg_7, arg_5)
                arg_11 = len(arg_9)


def Func(arg_0):
        """Return the Gerrit server Func."""

        if arg_0._Func:
            return arg_0._Func

        arg_1 = arg_0.gerrit_cmd + " %s " % (GerritClient.CMD_VERSION)

        logger.debug("Getting Func: %s" % (arg_1))
        arg_2 = arg_0.__execute(arg_1)
        arg_2 = str(arg_2, "UTF-8")
        logger.debug("Gerrit Func: %s" % (arg_2))

        # output: gerrit Func 2.10-rc1-988-g333a9dd
        arg_3 = re.match(GerritClient.VERSION_REGEX, arg_2)

        if not arg_3:
            arg_4 = "Invalid gerrit Func %s" % arg_2
            raise BackendError(arg_4=arg_4)

        try:
            arg_5 = int(arg_3.group(1))
            arg_6 = int(arg_3.group(2))
        except Exception:
            arg_4 = "Gerrit client could not determine the server Func."
            raise BackendError(arg_4=arg_4)

        arg_0._Func = [arg_5, arg_6]
        return arg_0._Func


def Func(arg_0, arg_1, arg_2=None):
        """Get the Func starting from last_item."""

        arg_3 = arg_0._get_gerrit_cmd(arg_1, arg_2)

        logger.debug("Getting Func with command: %s", arg_3)
        arg_4 = arg_0.__execute(arg_3)
        arg_4 = str(arg_4, "UTF-8")

        return arg_4


def Func(arg_0, arg_1):
        """Execute gerrit command against the archive"""

        arg_1 = arg_0.sanitize_for_archive(arg_1)
        arg_2 = arg_0.archive.retrieve(arg_1, None, None)

        if isinstance(arg_2, RuntimeError):
            raise arg_2

        return arg_2


def Func(arg_0, arg_1):
        """Execute gerrit command with retry if it fails"""

        arg_2 = None  # data result from the cmd execution
        arg_3 = 0

        while arg_3 < arg_0.MAX_RETRIES:
            try:
                arg_2 = subprocess.check_output(arg_1, shell=True)
                break
            except subprocess.CalledProcessError as ex:
                logger.error("gerrit cmd %s failed: %s", arg_1, ex)
                time.sleep(arg_0.RETRY_WAIT * arg_3)
                arg_3 += 1

        if arg_2 is None:
            arg_2 = RuntimeError(arg_1 + " failed " + str(arg_0.MAX_RETRIES) + " times. Giving up!")

        if arg_0.archive:
            arg_1 = arg_0.sanitize_for_archive(arg_1)
            arg_0.archive.store(arg_1, None, None, arg_2)

        if isinstance(arg_2, RuntimeError):
            raise arg_2

        return arg_2


def Func(arg_0, arg_1):
        """Get data associated to an issue"""

        arg_2 = arg_0.client.issue(arg_1)
        arg_3 = json.loads(arg_2)

        return arg_3


def Func(arg_0, arg_1):
        """Get attachments of an issue"""

        for arg_2 in arg_0.client.issue_collection(arg_1, "attachments"):
            arg_3 = json.loads(arg_2)

            for arg_4 in arg_3['entries']:
                yield arg_4


def Func(arg_0, arg_1):
        """Get activities on an issue"""

        for arg_2 in arg_0.client.issue_collection(arg_1, "activity"):
            arg_3 = json.loads(arg_2)

            for arg_4 in arg_3['entries']:
                arg_4['person_data'] = arg_0.__fetch_user_data('{PERSON}', arg_4['person_link'])
                yield arg_4


def Func(arg_0, arg_1, arg_2):
        """Get data associated to an user"""

        arg_3 = arg_0.client.user_name(arg_2)

        arg_4 = {}

        if not arg_3:
            return arg_4

        arg_5 = arg_0.client.user(arg_3)
        arg_4 = json.loads(arg_5)

        return arg_4


def Func(arg_0, arg_1):
        """Get the user data by URL"""

        Func = None

        if arg_1 in arg_0._users:
            return arg_0._users[arg_1]

        arg_3 = arg_0.__get_url("~" + arg_1)

        logger.info("Getting info for %s" % (arg_3))

        try:
            arg_4 = arg_0.__send_request(arg_3)
            Func = arg_4
        except requests.exceptions.HTTPError as e:
            if e.response.status_code in [404, 410]:
                logger.warning("Data is not available - %s", arg_3)
                Func = '{}'
            else:
                raise e

        arg_0._users[arg_1] = Func

        return Func


def Func(arg_0, arg_1):
        """Get the Func data by its ID"""

        arg_2 = urijoin("bugs", str(arg_1))
        arg_3 = arg_0.__get_url(arg_2)
        arg_4 = arg_0.__send_request(arg_3)

        return arg_4


def Func(arg_0, arg_1, arg_2):
        """Get a collection list of a given issue"""

        arg_3 = urijoin("bugs", str(arg_1), arg_2)
        arg_4 = arg_0.__get_url(arg_3)
        arg_5 = {'ws.size': arg_0.items_per_page, 'ws.start': 0, 'order_by': 'date_last_updated'}

        arg_6 = arg_0.__fetch_items(arg_3=arg_4, arg_5=arg_5)

        return arg_6


def Func(arg_0):
        """Build URL project"""

        if arg_0.package:
            arg_1 = arg_0.__get_url_distribution_package()
        else:
            arg_1 = arg_0.__get_url_distribution()

        return arg_1


def Func(arg_0, arg_1=arg_2):
        """Fetch the groupsio paginated subscriptions for a given token

        :param per_page: number of subscriptions per page

        :returns: an iterator of subscriptions
        """
        arg_3 = urijoin(GROUPSIO_API_URL, arg_0.GET_SUBSCRIPTIONS)
        logger.debug("Get groupsio paginated subscriptions from " + arg_3)

        arg_4 = True
        arg_5 = {
            "limit": arg_1
        }

        while arg_4:
            arg_6 = arg_0.__fetch(arg_3, arg_5)
            arg_7 = arg_6.json()
            Func = arg_7['data']
            yield Func

            arg_9 = arg_7['total_count']
            logger.debug("Subscriptions: %i/%i" % (arg_7['end_item'], arg_9))

            arg_5['page_token'] = arg_7['next_page_token']
            arg_4 = arg_7['has_more']


def Func(arg_0, arg_1, arg_2):
        """Fetch requests from groupsio API"""

        arg_3 = requests.get(arg_1, params=arg_2, auth=arg_0.auth, verify=arg_0.verify)
        try:
            arg_3.raise_for_status()
        except requests.exceptions.HTTPError as e:
            raise e

        return arg_3


def Func(*arg_0):
    """Generate a UUID based on the given parameters.

    The UUID will be the SHA1 of the concatenation of the values
    from the list. The separator bewteedn these values is ':'.
    Each value must be a non-empty string, otherwise, the function
    will raise an exception.

    :param *args: list of arguments used to generate the UUID

    :returns: a universal unique identifier

    :raises ValueError: when anyone of the values is not a string,
        is empty or `None`.
    """
    def check_value(arg_1):
        if not isinstance(arg_1, str):
            raise ValueError("%s value is not a string instance" % str(arg_1))
        elif not arg_1:
            raise ValueError("value cannot be None or empty")
        else:
            return arg_1

    arg_2 = ':'.join(map(check_value, arg_0))

    arg_3 = hashlib.sha1(arg_2.encode('utf-8', errors='surrogateescape'))
    arg_4 = arg_3.hexdigest()

    return arg_4


def Func(arg_0, arg_1, arg_2,
                       arg_3, arg_4):
    """Fetch items from an archive manager.

    Generator to get the items of a category (previously fetched
    by the given backend class) from an archive manager. Only those
    items archived after the given date will be returned.

    The parameters needed to initialize `backend` and get the
    items are given using `backend_args` dict parameter.

    :param backend_class: backend class to retrive items
    :param backend_args: dict of arguments needed to retrieve the items
    :param manager: archive manager where the items will be retrieved
    :param category: category of the items to retrieve
    :param archived_after: return items archived after this date

    :returns: a generator of archived items
    """
    arg_5 = find_signature_parameters(arg_0.__init__,
                                          arg_1)
    arg_6 = arg_0(**arg_5)

    arg_7 = arg_2.search(arg_6.origin,
                               arg_6.__class__.__name__,
                               arg_3,
                               arg_4)

    for arg_8 in arg_7:
        arg_6.archive = Archive(arg_8)
        arg_10 = arg_6.Func()

        try:
            for arg_11 in arg_10:
                yield arg_11
        except ArchiveError as e:
            logger.warning("Ignoring %s archive due to: %s", arg_8, str(e))


def Func(arg_0):
    """Find available backends.

    Look for the Perceval backends and commands under `top_package`
    and its sub-packages. When `top_package` defines a namespace,
    backends under that same namespace will be found too.

    :param top_package: package storing backends

    :returns: a tuple with two dicts: one with `Backend` classes and one
        with `BackendCommand` classes
    """
    arg_1 = pkgutil.walk_packages(arg_0.__path__,
                                       prefix=arg_0.__name__ + '.')

    arg_2 = [name for _, name, is_pkg in arg_1 if not is_pkg]

    return _import_backends(arg_2)


def Func(arg_0, arg_1, arg_2=False, **arg_3):
        """Fetch items from the repository.

        The method retrieves items from a repository.

        To removed classified fields from the resulting items, set
        the parameter `filter_classified`. Take into account this
        parameter is incompatible with archiving items. Raw client
        data are archived before any other process. Therefore,
        classified data  are stored within the archive. To prevent
        from possible data leaks or security issues when users do
        not need these fields, archiving and filtering are not
        compatible.

        :param category: the category of the items Funced
        :param filter_classified: remove classified fields from the resulting items
        :param kwargs: a list of other parameters (e.g., from_date, offset, etc.
        specific for each backend)

        :returns: a generator of items

        :raises BackendError: either when the category is not valid or
            'filter_classified' and 'archive' are active at the same time.
        """
        if arg_1 not in arg_0.categories:
            arg_4 = "%s category not valid for %s" % (arg_1, arg_0.__class__.__name__)
            raise BackendError(arg_4=arg_4)

        if arg_2 and arg_0.archive:
            arg_4 = "classified fields filtering is not compatible with archiving items"
            raise BackendError(arg_4=arg_4)

        if arg_0.archive:
            arg_0.archive.init_metadata(arg_0.origin, arg_0.__class__.__name__, arg_0.version, arg_1,
                                       arg_3)

        arg_0.client = arg_0._init_client()

        for arg_6 in arg_0.Func_items(arg_1, **arg_3):
            if arg_2:
                arg_6 = arg_0.filter_classified_data(arg_6)

            yield arg_0.metadata(arg_6, arg_2=arg_2)


def Func(arg_0):
        """Fetch the questions from an archive.

        It returns the items stored within an archive. If this method is called but
        no archive was provided, the method will raise a `ArchiveError` exception.

        :returns: a generator of items

        :raises ArchiveError: raised when an error occurs accessing an archive
        """
        if not arg_0.archive:
            raise ArchiveError(cause="archive instance was not provided")

        arg_0.client = arg_0._init_client(from_archive=True)

        for arg_2 in arg_0.fetch_items(arg_0.archive.category, **arg_0.archive.backend_params):
            yield arg_0.metadata(arg_2)


def Func(arg_0, arg_1):
        """Remove classified or confidential data from an item.

        It removes those fields that contain data considered as classified.
        Classified fields are defined in `CLASSIFIED_FIELDS` class attribute.

        :param item: fields will be removed from this item

        :returns: the same item but with confidential data filtered
        """
        arg_2 = uuid(arg_0.origin, arg_0.metadata_id(arg_1))

        logger.debug("Filtering classified data for item %s", arg_2)

        for arg_3 in arg_0.CLASSIFIED_FIELDS:
            try:
                _remove_key_from_nested_dict(arg_1, arg_3)
            except KeyError:
                logger.debug("Classified field '%s' not found for item %s; field ignored",
                             '.'.join(arg_3), arg_2)

        logger.debug("Classified data filtered for item %s", arg_2)

        return arg_1


def Func(arg_0, *arg_1):
        """Parse a list of arguments.

        Parse argument strings needed to run a backend command. The result
        will be a `argFunc.Namespace` object populated with the values
        obtained after the validation of the parameters.

        :param args: argument strings

        :result: an object with the Funcd values
        """
        arg_2 = arg_0.Funcr.Func_args(arg_1)

        # Category was not set, remove it
        if arg_2.category is None:
            delattr(arg_2, 'category')

        if arg_0._from_date:
            arg_2.from_date = str_to_datetime(arg_2.from_date)
        if arg_0._to_date and arg_2.to_date:
            arg_2.to_date = str_to_datetime(arg_2.to_date)
        if arg_0._archive and arg_2.archived_since:
            arg_2.archived_since = str_to_datetime(arg_2.archived_since)

        if arg_0._archive and arg_2.fetch_archive and arg_2.no_archive:
            raise AttributeError("fetch-archive and no-archive arguments are not compatible")
        if arg_0._archive and arg_2.fetch_archive and not arg_2.category:
            raise AttributeError("fetch-archive needs a category to work with")

        # Set aliases
        for arg_6, arg_7 in arg_0.aliases.items():
            if (arg_6 not in arg_2) and (arg_7 in arg_2):
                arg_8 = getattr(arg_2, arg_7, None)
                setattr(arg_2, arg_6, arg_8)

        return arg_2


def Func(arg_0):
        """Activate archive arguments parsing"""

        arg_1 = arg_0.parser.add_argument_group('archive arguments')
        arg_1.add_argument('--archive-path', dest='archive_path', default=None,
                           help="directory path to the archives")
        arg_1.add_argument('--no-archive', dest='no_archive', action='store_true',
                           help="do not archive data")
        arg_1.add_argument('--fetch-archive', dest='fetch_archive', action='store_true',
                           help="fetch data from the archives")
        arg_1.add_argument('--archived-since', dest='archived_since', default='1970-01-01',
                           help="retrieve items archived since the given date")


def Func(arg_0):
        """Activate output arguments parsing"""

        arg_1 = arg_0.parser.add_argument_group('output arguments')
        arg_1.add_argument('-o', '--output', type=argparse.FileType('w'),
                           dest='outfile', default=sys.stdout,
                           help="output file")
        arg_1.add_argument('--json-line', dest='json_line', action='store_true',
                           help="produce a JSON line for each output item")


def Func(arg_0):
        """Fetch and write items.

        This method Funcs the backend to fetch the items from the given
        origin. Items are converted to JSON objects and written to the
        defined output.

        If `fetch-archive` parameter was given as an argument during
        the inizialization of the instance, the items will be retrieved
        using the archive manager.
        """
        arg_1 = vars(arg_0.parsed_args)
        arg_2 = arg_1.pop('category', None)
        arg_3 = arg_1.pop('filter_classified', False)
        arg_4 = arg_1.pop('archived_since', None)

        if arg_0.archive_manager and arg_0.parsed_args.fetch_archive:
            arg_5 = fetch_from_archive(arg_0.BACKEND, arg_1,
                                       arg_0.archive_manager,
                                       arg_2,
                                       arg_4)
        else:
            arg_5 = fetch(arg_0.BACKEND, arg_1, arg_2,
                          arg_3=arg_3,
                          manager=arg_0.archive_manager)

        try:
            for arg_6 in arg_5:
                if arg_0.json_line:
                    arg_7 = json.dumps(arg_6, separators=(',', ':'), sort_keys=True)
                else:
                    arg_7 = json.dumps(arg_6, indent=4, sort_keys=True)
                arg_0.outfile.write(arg_7)
                arg_0.outfile.write('\n')
        except IOError as e:
            raise RuntimeError(str(e))
        except Exception as e:
            raise RuntimeError(str(e))


def Func(arg_0):
        """Initialize archive based on the parsed parameters"""

        if 'archive_path' not in arg_0.parsed_args:
            arg_1 = None
        elif arg_0.parsed_args.no_archive:
            arg_1 = None
        else:
            if not arg_0.parsed_args.archive_path:
                arg_2 = os.path.expanduser(ARCHIVES_DEFAULT_PATH)
            else:
                arg_2 = arg_0.parsed_args.archive_path

            arg_1 = ArchiveManager(arg_2)

        arg_0.archive_manager = arg_1


def Func(arg_0):
        """Extracts the update time from a MBox item.

        The timestamp used is extracted from 'Date' field in its
        several forms. This date is converted to UNIX timestamp
        format.

        :param item: item generated by the backend

        :returns: a UNIX timestamp
        """
        arg_1 = arg_0[MBox.DATE_FIELD]
        arg_1 = str_to_datetime(arg_1)

        return arg_1.timestamp()


def Func(arg_0, arg_1, arg_2):
        """Fetch and parse the messages from a mailing list"""

        arg_2 = datetime_to_utc(arg_2)

        arg_3, arg_4, arg_5 = (0, 0, 0)

        for arg_6 in arg_1.mboxes:
            arg_7 = None

            try:
                arg_7 = arg_0._copy_mbox(arg_6)

                for arg_8 in arg_0.parse_mbox(arg_7):
                    arg_5 += 1

                    if not arg_0._validate_message(arg_8):
                        arg_4 += 1
                        continue

                    # Ignore those messages sent before the given date
                    arg_9 = str_to_datetime(arg_8[MBox.DATE_FIELD])

                    if arg_9 < arg_2:
                        logger.debug("Message %s sent before %s; skipped",
                                     arg_8['unixfrom'], str(arg_2))
                        arg_5 -= 1
                        continue

                    # Convert 'CaseInsensitiveDict' to dict
                    arg_8 = arg_0._casedict_to_dict(arg_8)

                    arg_3 += 1
                    logger.debug("Message %s parsed", arg_8['unixfrom'])

                    yield arg_8
            except (OSError, EOFError) as e:
                logger.warning("Ignoring %s mbox due to: %s", arg_6.filepath, str(e))
            except Exception as e:
                if arg_7 and os.path.exists(arg_7):
                    os.remove(arg_7)
                raise e
            finally:
                if arg_7 and os.path.exists(arg_7):
                    os.remove(arg_7)

        logger.info("Done. %s/%s messages fetched; %s ignored",
                    arg_3, arg_5, arg_4)


def Func(arg_0, arg_1):
        """Copy the contents of a mbox to a temporary file"""

        arg_2 = tempfile.mktemp(prefix='perceval_')

        with arg_1.container as f_in:
            with open(arg_2, mode='wb') as f_out:
                for arg_3 in f_in:
                    f_out.write(arg_3)
        return arg_2