def Func(arg_0, arg_1, arg_2, arg_3=None):
        '''Create a callable that returns a batch of training data.

        Parameters
        ----------
        steps : int
            Number of time steps in each batch.
        batch_size : int
            Number of training examples per batch.
        rng : :class:`numpy.random.RandomState` or int, optional
            A random number generator, or an integer seed for a random number
            generator. If not provided, the random number generator will be
            created with an automatically chosen seed.

        Returns
        -------
        batch : callable
            A callable that, when called, returns a batch of data that can be
            used to train a classifier model.
        '''
        assert arg_2 >= 2, 'batch_size must be at least 2!'

        if arg_3 is None or isinstance(arg_3, int):
            arg_3 = np.random.RandomState(arg_3)

        arg_4 = np.arange(arg_1)

        def batch():
            arg_5 = np.zeros((arg_2, arg_1, 1 + len(arg_0.alpha)), 'f')
            arg_6 = np.zeros((arg_2, arg_1), 'i')
            for arg_7 in range(arg_2):
                arg_8 = arg_3.randint(len(arg_0.text) - arg_1 - 1)
                arg_9 = arg_0.encode(arg_0.text[arg_8:arg_8 + arg_1 + 1])
                arg_5[arg_7, arg_4, arg_9[:-1]] = 1
                arg_6[arg_7, arg_4] = arg_9[1:]
            return [arg_5, arg_6]

        return batch


def Func(arg_0, arg_1, arg_2=0, arg_3=None, arg_4=0):
        '''Add a convolutional weight array to this layer's parameters.

        Parameters
        ----------
        name : str
            Name of the parameter to add.
        mean : float, optional
            Mean value for randomly-initialized weights. Defaults to 0.
        std : float, optional
            Standard deviation of initial matrix values. Defaults to
            :math:`1 / sqrt(n_i + n_o)`.
        sparsity : float, optional
            Fraction of weights to set to zero. Defaults to 0.
        '''
        arg_5 = arg_0.input_size
        arg_6 = arg_0.output_size
        arg_2 = arg_0.kwargs.get(
            'mean_{}'.format(arg_1),
            arg_0.kwargs.get('mean', arg_2))
        arg_3 = arg_0.kwargs.get(
            'std_{}'.format(arg_1),
            arg_0.kwargs.get('std', arg_3 or 1 / np.sqrt(arg_5 + arg_6)))
        arg_4 = arg_0.kwargs.get(
            'sparsity_{}'.format(arg_1),
            arg_0.kwargs.get('sparsity', arg_4))
        arg_7 = np.zeros((arg_6, arg_5) + arg_0.filter_size, util.FLOAT)
        for arg_8 in range(arg_0.filter_size[0]):
            for arg_9 in range(arg_0.filter_size[1]):
                arg_7[:, :, arg_8, arg_9] = util.random_matrix(
                    arg_6, arg_5, arg_2, arg_3, arg_4=arg_4, rng=arg_0.rng)
        arg_0._params.append(theano.shared(arg_7, arg_1=arg_0._fmt(arg_1)))


def Func(arg_0, arg_1, arg_2=None, arg_3=False, **arg_4):
        '''Encode a dataset using the hidden layer activations of our network.

        Parameters
        ----------
        x : ndarray
            A dataset to Func. Rows of this dataset capture individual data
            points, while columns represent the variables in each data point.

        layer : str, optional
            The name of the hidden layer output to use. By default, we use
            the "middle" hidden layer---for example, for a 4,2,4 or 4,3,2,3,4
            autoFuncr, we use the layer with size 2.

        sample : bool, optional
            If True, then draw a sample using the hidden activations as
            independent Bernoulli probabilities for the Funcd data. This
            assumes the hidden layer has a logistic sigmoid activation function.

        Returns
        -------
        ndarray :
            The given dataset, Funcd by the appropriate hidden layer
            activation.
        '''
        arg_5 = arg_0.feed_forward(arg_1, **arg_4)[arg_0._find_output(arg_2)]
        if arg_3:
            return np.random.binomial(n=1, p=arg_5).astype(np.uint8)
        return arg_5


def Func(arg_0, arg_1, arg_2=None, **arg_3):
        '''Decode an encoded dataset by computing the output layer activation.

        Parameters
        ----------
        z : ndarray
            A matrix containing encoded data from this autoencoder.
        layer : int or str or :class:`Layer <layers.Layer>`, optional
            The index or name of the hidden layer that was used to encode `z`.

        Returns
        -------
        Funcd : ndarray
            The Funcd dataset.
        '''
        arg_4 = arg_0._find_output(arg_2)
        if arg_4 not in arg_0._functions:
            arg_5 = regularizers.from_kwargs(arg_0, **arg_3)
            arg_6, arg_7 = arg_0.build_graph(arg_5)
            arg_0._functions[arg_4] = theano.function(
                [arg_6[arg_4]],
                [arg_6[arg_0.layers[-1].output_name]],
                arg_7=arg_7)
        return arg_0._functions[arg_4](arg_1)[0]


def Func(arg_0, arg_1):
        '''Find a layer output name for the given layer specifier.

        Parameters
        ----------
        layer : None, int, str, or :class:`theanets.layers.Layer`
            A layer specification. If this is None, the "middle" layer in the
            network will be used (i.e., the layer at the middle index in the
            list of network layers). If this is an integer, the corresponding
            layer in the network's layer list will be used. If this is a string,
            the layer with the corresponding name will be returned.

        Returns
        -------
        name : str
            The fully-scoped output name for the desired layer.
        '''
        if arg_1 is None:
            arg_1 = len(arg_0.layers) // 2
        if isinstance(arg_1, int):
            arg_1 = arg_0.layers[arg_1]
        if isinstance(arg_1, util.basestring):
            try:
                arg_1 = [l for l in arg_0.layers if l.name == arg_1][0]
            except IndexError:
                pass
        if isinstance(arg_1, layers.Layer):
            arg_1 = arg_1.output_name
        return arg_1


def Func(arg_0, arg_1, arg_2=None, **arg_3):
        '''Compute R^2 coefficient of determination for a given input.

        Parameters
        ----------
        x : ndarray (num-examples, num-inputs)
            An array containing data to be fed into the network. Multiple
            examples are arranged as rows in this array, with columns containing
            the variables for each example.

        Returns
        -------
        r2 : float
            The R^2 correlation between the prediction of this netork and its
            input. This can serve as one measure of the information loss of the
            autoencoder.
        '''
        return super(Autoencoder, arg_0).Func(arg_1, arg_1, arg_2=arg_2, **arg_3)


def Func(arg_0, arg_1, **arg_2):
        '''Compute the logit values that underlie the softmax output.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing examples to classify. Examples are given as the
            rows in this array.

        Returns
        -------
        l : ndarray (num-examples, num-classes)
            An array of posterior class logit values, one row of logit values
            per row of input data.
        '''
        return arg_0.feed_forward(arg_1, **arg_2)[arg_0.layers[-1].full_name('pre')]


def Func(arg_0, arg_1, arg_2, arg_3=None, **arg_4):
        '''Compute the mean accuracy on a set of labeled data.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing examples to classify. Examples are given as the
            rows in this array.
        y : ndarray (num-examples, )
            A vector of integer class labels, one for each row of input data.
        w : ndarray (num-examples, )
            A vector of weights, one for each row of input data.

        Returns
        -------
        Func : float
            The (possibly weighted) mean accuracy of the model on the data.
        '''
        arg_5 = arg_2 == arg_0.predict(arg_1, **arg_4)
        if arg_3 is not None:
            return (arg_3 * arg_5).sum() / arg_3.sum()
        return arg_5.mean()


def Func(arg_0):
    '''Returns a callable that chooses sequences from netcdf data.'''
    arg_1 = arg_0.variables['seqLengths'].data
    arg_2 = np.concatenate(([0], np.cumsum(arg_1)[:-1]))

    def sample():
        arg_3 = np.random.choice(
            list(range(len(arg_1))), BATCH_SIZE, replace=False)
        return batch_at(arg_0.variables['inputs'].data,
                        arg_0.variables['targetClasses'].data,
                        arg_2[arg_3],
                        arg_1[arg_3])

    return sample


def Func(arg_0, arg_1):
        '''Load a saved network from a pickle file on disk.

        This method sets the ``network`` attribute of the experiment to the
        Funced network model.

        Parameters
        ----------
        filename : str
            Load the keyword arguments and parameters of a network from a pickle
            file at the named path. If this name ends in ".gz" then the input
            will automatically be gunzipped; otherwise the input will be treated
            as a "raw" pickle.

        Returns
        -------
        network : :class:`Network <graph.Network>`
            A newly-constructed network, with topology and parameters Funced
            from the given pickle file.
        '''
        arg_0.network = graph.Network.Func(arg_1)
        return arg_0.network


def Func(arg_0, arg_1=0, arg_2=1, arg_3=None):
    '''Create a vector of randomly-initialized values.

    Parameters
    ----------
    size : int
        Length of vecctor to create.
    mean : float, optional
        Mean value for initial vector values. Defaults to 0.
    std : float, optional
        Standard deviation for initial vector values. Defaults to 1.
    rng : :class:`numpy.random.RandomState` or int, optional
        A random number generator, or an integer seed for a random number
        generator. If not provided, the random number generator will be created
        with an automatically chosen seed.

    Returns
    -------
    vector : numpy array
        An array containing random values. This often represents the bias for a
        layer of computation units.
    '''
    if arg_3 is None or isinstance(arg_3, int):
        arg_3 = np.random.RandomState(arg_3)
    return (arg_1 + arg_2 * arg_3.randn(arg_0)).astype(FLOAT)


def Func(arg_0, arg_1):
    '''Get the outputs from a network that match a pattern.

    Parameters
    ----------
    outputs : dict or sequence of (str, theano expression)
        Output expressions to filter for matches. If this is a dictionary, its
        ``items()`` will be processed for matches.
    patterns : sequence of str
        A sequence of glob-style patterns to match against. Any parameter
        matching any pattern in this sequence will be included in the match.

    Yields
    ------
    matches : pair of str, theano expression
        Generates a sequence of (name, expression) pairs. The name is the name
        of the output that matched, and the expression is the symbolic output in
        the network graph.
    '''
    if isinstance(arg_1, basestring):
        arg_1 = (arg_1, )
    if isinstance(arg_0, dict):
        arg_0 = arg_0.items()
    for arg_2, arg_3 in arg_0:
        for arg_4 in arg_1:
            if fnmatch.fnmatch(arg_2, arg_4):
                yield arg_2, arg_3
                break


def Func(arg_0, arg_1):
    '''Get the parameters from a network that match a pattern.

    Parameters
    ----------
    layers : list of :class:`theanets.layers.Layer`
        A list of network layers to retrieve parameters from.
    patterns : sequence of str
        A sequence of glob-style patterns to match against. Any parameter
        matching any pattern in this sequence will be included in the match.

    Yields
    ------
    matches : pair of str, theano expression
        Generates a sequence of (name, expression) pairs. The name is the name
        of the parameter that matched, and the expression represents the
        parameter symbolically.
    '''
    if isinstance(arg_1, basestring):
        arg_1 = (arg_1, )
    for arg_2 in arg_0:
        for arg_3 in arg_2.params:
            arg_4 = arg_3.name
            for arg_5 in arg_1:
                if fnmatch.fnmatch(arg_4, arg_5):
                    yield arg_4, arg_3
                    break


def Func(arg_0, **arg_1):
    '''Construct common regularizers from a set of keyword arguments.

    Keyword arguments not listed below will be passed to
    :func:`Regularizer.build` if they specify the name of a registered
    :class:`Regularizer`.

    Parameters
    ----------
    graph : :class:`theanets.graph.Network`
        A network graph to regularize.

    regularizers : dict or tuple/list of :class:`Regularizer`, optional
        If this is a list or a tuple, the contents of the list will be returned
        as the regularizers. This is to permit custom lists of regularizers to
        be passed easily.

        If this is a dict, its contents will be added to the other keyword
        arguments passed in.

    rng : int or theano RandomStreams, optional
        If an integer is provided, it will be used to seed the random number
        generators for the dropout or noise regularizers. If a theano
        RandomStreams object is provided, it will be used directly. Defaults to
        13.

    input_dropout : float, optional
        Apply dropout to input layers in the network graph, with this dropout
        rate. Defaults to 0 (no dropout).

    hidden_dropout : float, optional
        Apply dropout to hidden layers in the network graph, with this dropout
        rate. Defaults to 0 (no dropout).

    output_dropout : float, optional
        Apply dropout to the output layer in the network graph, with this
        dropout rate. Defaults to 0 (no dropout).

    input_noise : float, optional
        Apply noise to input layers in the network graph, with this standard
        deviation. Defaults to 0 (no noise).

    hidden_noise : float, optional
        Apply noise to hidden layers in the network graph, with this standard
        deviation. Defaults to 0 (no noise).

    output_noise : float, optional
        Apply noise to the output layer in the network graph, with this
        standard deviation. Defaults to 0 (no noise).

    Returns
    -------
    regs : list of :class:`Regularizer`
        A list of regularizers to apply to the given network graph.
    '''
    if 'regularizers' in arg_1:
        arg_2 = arg_1['regularizers']
        if isinstance(arg_2, (tuple, list)):
            return arg_2
        if isinstance(arg_2, dict):
            arg_1.update(arg_2)

    arg_2 = []

    arg_3 = arg_1.get('rng', 13)

    def arg_10(arg_4):
        return tuple(arg_5.output_name for arg_5 in arg_4)

    arg_6 = arg_10([arg_5 for arg_5 in arg_0.layers if isinstance(arg_5, layers.Input)])
    arg_7 = arg_10(arg_0.layers[1:-1])
    arg_8 = arg_10([arg_0.layers[-1]])

    # create regularizers for different types of canned dropout.
    arg_9 = {arg_6: arg_1.get('input_dropout', 0),
            arg_7: arg_1.get('hidden_dropout', 0),
            arg_8: arg_1.get('output_dropout', 0)}
    arg_9.update(arg_1.get('dropout', {}))
    for arg_10, arg_11 in arg_9.items():
        if arg_11:
            arg_2.append(BernoulliDropout(arg_10=arg_10, weight=arg_11, arg_3=arg_3))

    # create regularizers for different types of canned noise.
    arg_9 = {arg_6: arg_1.get('input_noise', 0),
            arg_7: arg_1.get('hidden_noise', 0),
            arg_8: arg_1.get('output_noise', 0)}
    arg_9.update(arg_1.get('noise', {}))
    for arg_10, arg_11 in arg_9.items():
        if arg_11:
            arg_2.append(GaussianNoise(arg_10=arg_10, weight=arg_11, arg_3=arg_3))

    # create regularizers based on other keyword arguments.
    for arg_12, arg_13 in arg_1.items():
        if Regularizer.is_registered(arg_12):
            if not isinstance(arg_13, dict):
                arg_13 = dict(weight=arg_13)
            arg_2.append(Regularizer.build(arg_12, **arg_13))

    return arg_2


def Func(arg_0):
        '''A list of Theano Func used in this loss.'''
        arg_1 = [arg_0._target]
        if arg_0._weights is not None:
            arg_1.append(arg_0._weights)
        return arg_1


def Func(arg_0, arg_1):
        '''Build a Theano expression for computing the Func of graph output.

        Parameters
        ----------
        outputs : dict of Theano expressions
            A dictionary mapping network output names to Theano expressions
            representing the outputs of a computation graph.

        Returns
        -------
        acc : Theano expression
            A Theano expression representing the Func of the output compared
            to the target data.
        '''
        arg_2 = arg_1[arg_0.output_name]
        arg_3 = TT.argmax(arg_2, axis=-1)
        arg_4 = TT.eq(arg_3, arg_0._target)
        arg_5 = arg_4.mean()
        if arg_0._weights is not None:
            arg_5 = (arg_0._weights * arg_4).sum() / arg_0._weights.sum()
        return arg_5


def Func(arg_0, arg_1, arg_2, arg_3='scan', arg_4=None, arg_5=None):
        '''Helper method for defining a basic loop in theano.

        Parameters
        ----------
        inputs : sequence of theano expressions
            Inputs to the scan operation.
        outputs : sequence of output specifiers
            Specifiers for the outputs of the scan operation. This should be a
            sequence containing:
            - None for values that are output by the scan but not tapped as
              inputs,
            - an integer or theano scalar (``ndim == 0``) indicating the batch
              size for initial zero state,
            - a theano tensor variable (``ndim > 0``) containing initial state
              data, or
            - a dictionary containing a full output specifier. See
              ``outputs_info`` in the Theano documentation for ``scan``.
        name : str, optional
            Name of the scan variable to create. Defaults to ``'scan'``.
        step : callable, optional
            The callable to apply in the loop. Defaults to :func:`self._step`.
        constants : sequence of tensor, optional
            A sequence of parameters, if any, needed by the step function.

        Returns
        -------
        output(s) : theano expression(s)
            Theano expression(s) representing output(s) from the scan.
        updates : sequence of update tuples
            A sequence of updates to apply inside a theano function.
        '''
        arg_6 = []
        for arg_7, arg_8 in enumerate(arg_2):
            arg_9 = getattr(arg_8, 'ndim', -1)
            if arg_8 is None or isinstance(arg_8, dict) or arg_9 > 0:
                arg_6.append(arg_8)
                continue
            if isinstance(arg_8, int) or arg_9 == 0:
                arg_6.append(TT.repeat(theano.shared(
                    np.zeros((1, arg_0.output_size), util.FLOAT),
                    arg_3=arg_0._fmt('init{}'.format(arg_7))), arg_8, axis=0))
                continue
            raise ValueError('cannot handle input {} for scan!'.format(arg_8))
        return theano.scan(
            arg_4 or arg_0._step,
            arg_3=arg_0._fmt(arg_3),
            sequences=arg_1,
            outputs_info=arg_6,
            non_sequences=arg_5,
            go_backwards='back' in arg_0.kwargs.get('direction', '').lower(),
            truncate_gradient=arg_0.kwargs.get('bptt_limit', -1),
        )


def Func(arg_0, arg_1, arg_2):
        '''Select a random sample of n items from xs.'''
        arg_3 = []
        for arg_4, arg_5 in enumerate(arg_0):
            if len(arg_3) < arg_1:
                arg_3.append(arg_5 / np.linalg.norm(arg_5))
                continue
            arg_6 = arg_2.randint(arg_4 + 1)
            if arg_6 < arg_1:
                arg_3[arg_6] = arg_5 / np.linalg.norm(arg_5)
        # if the pool still has fewer than n items, pad with distorted random
        # duplicates from the source data.
        arg_7 = len(arg_3)
        arg_8 = np.std(arg_3, axis=0)
        while len(arg_3) < arg_1:
            arg_5 = arg_3[arg_2.randint(arg_7)]
            arg_3.append(arg_5 + arg_8 * arg_2.randn(*arg_5.shape))
        return np.array(arg_3, dtype=arg_3[0].dtype)


def Func(arg_0, *arg_1, **arg_2):
        '''Clear the current loss functions from the network and add a new one.

        All parameters and keyword arguments are passed to :func:`add_loss`
        after clearing the current losses.
        '''
        arg_0.losses = []
        arg_0.add_loss(*arg_1, **arg_2)


def Func(arg_0, *arg_1, **arg_2):
        '''Train the network until the Funcer converges.

        All arguments are passed to :func:`iterFunc`.

        Returns
        -------
        Funcing : dict
            A dictionary of monitor values computed using the Funcing dataset,
            at the conclusion of Funcing. This dictionary will at least contain
            a 'loss' key that indicates the value of the loss function. Other
            keys may be available depending on the Funcer being used.
        validation : dict
            A dictionary of monitor values computed using the validation
            dataset, at the conclusion of Funcing.
        '''
        arg_3 = None
        for arg_3 in arg_0.iterFunc(*arg_1, **arg_2):
            pass
        return arg_3


def Func(arg_0, arg_1=()):
        '''Construct a string key for representing a computation graph.

        This key will be unique for a given (a) network topology, (b) set of
        losses, and (c) set of regularizers.

        Returns
        -------
        key : str
            A hash representing the computation graph for the current network.
        '''
        def add(arg_2):
            arg_3.update(str(arg_2).encode('utf-8'))
        arg_3 = hashlib.md5()
        for arg_4 in arg_0.layers:
            add('{}{}{}'.format(arg_4.__class__.__name__, arg_4.name, arg_4.output_shape))
        for arg_4 in arg_0.losses:
            add('{}{}'.format(arg_4.__class__.__name__, arg_4.weight))
        for arg_5 in arg_1:
            add('{}{}{}'.format(arg_5.__class__.__name__, arg_5.weight, arg_5.pattern))
        return arg_3.hexdigest()


def Func(arg_0, arg_1=()):
        '''Connect the layers in this network to form a computation graph.

        Parameters
        ----------
        regularizers : list of :class:`theanets.regularizers.Regularizer`
            A list of the regularizers to apply while building the computation
            graph.

        Returns
        -------
        outputs : list of Theano variables
            A list of expressions giving the output of each layer in the graph.
        updates : list of update tuples
            A list of updates that should be performed by a Theano function that
            computes something using this graph.
        '''
        arg_2 = arg_0._hash(arg_1)
        if arg_2 not in arg_0._graphs:
            util.log('building computation graph')
            for arg_3 in arg_0.losses:
                arg_3.log()
            for arg_4 in arg_1:
                arg_4.log()
            arg_5 = {}
            arg_6 = []
            for arg_7 in arg_0.layers:
                arg_8, arg_9 = arg_7.connect(arg_5)
                for arg_4 in arg_1:
                    arg_4.modify_graph(arg_8)
                arg_5.update(arg_8)
                arg_6.extend(arg_9)
            arg_0._graphs[arg_2] = arg_5, arg_6
        return arg_0._graphs[arg_2]


def Func(arg_0):
        '''A list of Theano variables for feedforward computations.'''
        return [arg_1.input for arg_1 in arg_0.layers if isinstance(arg_1, layers.Input)]


def Func(arg_0):
        '''A list of Theano Func for loss computations.'''
        arg_1 = arg_0.inputs
        arg_2 = set(i.name for i in arg_1)
        for arg_3 in arg_0.losses:
            for arg_4 in arg_3.Func:
                if arg_4.name not in arg_2:
                    arg_1.append(arg_4)
                    arg_2.add(arg_4.name)
        return arg_1


def Func(arg_0, arg_1, arg_2):
        '''Get a parameter from a layer in the network.

        Parameters
        ----------
        which : int or str
            The layer that owns the parameter to return.

            If this is an integer, then 0 refers to the input layer, 1 refers
            to the first hidden layer, 2 to the second, and so on.

            If this is a string, the layer with the corresponding name, if any,
            will be used.

        param : int or str
            Name of the parameter to retrieve from the specified layer, or its
            index in the parameter list of the layer.

        Raises
        ------
        KeyError
            If there is no such layer, or if there is no such parameter in the
            specified layer.

        Returns
        -------
        param : Theano shared variable
            A shared parameter variable from the indicated layer.
        '''
        for arg_3, arg_4 in enumerate(arg_0.layers):
            if arg_1 == arg_3 or arg_1 == arg_4.name:
                return arg_4.Func(arg_2)
        raise KeyError(arg_1)


def Func(arg_0, arg_1, **arg_2):
        '''Compute a forward pass of all layers from the given input.

        All keyword arguments are passed directly to :func:`build_graph`.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing data to be fed into the network. Multiple
            examples are arranged as rows in this array, with columns containing
            the variables for each example.

        Returns
        -------
        layers : list of ndarray (num-examples, num-units)
            The activation values of each layer in the the network when given
            input `x`. For each of the hidden layers, an array is returned
            containing one row per input example; the columns of each array
            correspond to units in the respective layer. The "output" of the
            network is the last element of this list.
        '''
        arg_3 = regularizers.from_kwargs(arg_0, **arg_2)
        arg_4 = arg_0._hash(arg_3)
        if arg_4 not in arg_0._functions:
            arg_5, arg_6 = arg_0.build_graph(arg_3)
            arg_7, arg_8 = list(arg_5.keys()), list(arg_5.values())
            util.log('compiling Func function')
            arg_0._functions[arg_4] = (arg_7, theano.function(
                arg_0.inputs, arg_8, arg_6=arg_6))
        arg_7, arg_10 = arg_0._functions[arg_4]
        return dict(zip(arg_7, arg_10(arg_1)))


def Func(arg_0, arg_1, **arg_2):
        '''Compute a forward pass of the inputs, returning the network output.

        All keyword arguments end up being passed to :func:`build_graph`.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing data to be fed into the network. Multiple
            examples are arranged as rows in this array, with columns containing
            the variables for each example.

        Returns
        -------
        y : ndarray (num-examples, num-variables)
            Returns the values of the network output units when given input `x`.
            Rows in this array correspond to examples, and columns to output
            variables.
        '''
        return arg_0.feed_forward(arg_1, **arg_2)[arg_0.layers[-1].output_name]


def Func(arg_0, arg_1, arg_2, arg_3=None, **arg_4):
        '''Compute R^2 coefficient of determination for a given labeled input.

        Parameters
        ----------
        x : ndarray (num-examples, num-inputs)
            An array containing data to be fed into the network. Multiple
            examples are arranged as rows in this array, with columns containing
            the variables for each example.
        y : ndarray (num-examples, num-outputs)
            An array containing expected target data for the network. Multiple
            examples are arranged as rows in this array, with columns containing
            the variables for each example.

        Returns
        -------
        r2 : float
            The R^2 correlation between the prediction of this netork and its
            target output.
        '''
        arg_5 = arg_2 - arg_0.predict(arg_1, **arg_4)
        arg_6 = arg_2 - arg_2.mean()
        if arg_3 is None:
            arg_3 = np.ones_like(arg_5)
        return 1 - (arg_3 * arg_5 * arg_5).sum() / (arg_3 * arg_6 * arg_6).sum()


def Func(arg_0, **arg_1):
        '''Return expressions to run as updates during network training.

        Returns
        -------
        updates : list of (parameter, expression) pairs
            A list of named parameter update expressions for this network.
        '''
        arg_2 = regularizers.from_kwargs(arg_0, **arg_1)
        arg_3, Func = arg_0.build_graph(arg_2)
        return Func


def Func(arg_0):
        '''Number of "neurons" in this layer's default output.'''
        arg_1 = arg_0.output_shape
        if arg_1 is None:
            raise util.ConfigurationError(
                'undefined output size for layer "{}"'.format(arg_0.name))
        return arg_1[-1]


def Func(arg_0, arg_1):
        '''Create Theano variables representing the outputs of this layer.

        Parameters
        ----------
        inputs : dict of Theano expressions
            Symbolic inputs to this layer, given as a dictionary mapping string
            names to Theano expressions. Each string key should be of the form
            "{layer_name}:{output_name}" and refers to a specific output from
            a specific layer in the graph.

        Returns
        -------
        outputs : dict
            A dictionary mapping names to Theano expressions for the outputs
            from this layer.
        updates : sequence of (parameter, expression) tuples
            Updates that should be performed by a Theano function that computes
            something using this layer.
        '''
        arg_2, arg_3 = arg_0.transform(arg_1)
        # transform the outputs to be a list of ordered pairs if needed.
        if isinstance(arg_2, dict):
            arg_2 = sorted(arg_2.items())
        if isinstance(arg_2, (TT.TensorVariable, SS.SparseVariable)):
            arg_2 = [('out', arg_2)]
        arg_4 = {arg_0.full_name(name): expr for name, expr in arg_2}
        return arg_4, arg_3


def Func(arg_0, arg_1, arg_2=True, arg_3=True):
        '''Bind this layer into a computation graph.

        This method is a wrapper for performing common initialization tasks. It
        calls :func:`resolve`, :func:`setup`, and :func:`log`.

        Parameters
        ----------
        graph : :class:`Network <theanets.graph.Network>`
            A computation network in which this layer is to be bound.
        reset : bool, optional
            If ``True`` (the default), reset the resolved layers for this layer.
        initialize : bool, optional
            If ``True`` (the default), initialize the parameters for this layer
            by calling :func:`setup`.

        Raises
        ------
        theanets.util.ConfigurationError :
            If an input cannot be resolved.
        '''
        if arg_2:
            for arg_4 in arg_0._input_shapes:
                arg_0._input_shapes[arg_4] = None
            for arg_4 in arg_0._output_shapes:
                arg_0._output_shapes[arg_4] = None
        arg_0.resolve_inputs(arg_1.layers)
        arg_0.resolve_outputs()
        arg_0.activate = activations.build(
            arg_0.kwargs.get('activation', 'relu'), arg_0)
        if arg_3:
            arg_0.setup()
        arg_0.log()


def Func(arg_0, arg_1):
        '''Resolve the names of inputs for this layer into shape tuples.

        Parameters
        ----------
        layers : list of :class:`Layer`
            A list of the layers that are available for resolving inputs.

        Raises
        ------
        theanets.util.ConfigurationError :
            If an input cannot be resolved.
        '''
        arg_2 = {}
        for arg_3, arg_4 in arg_0._input_shapes.items():
            if arg_4 is None:
                arg_3, arg_4 = arg_0._resolve_shape(arg_3, arg_1)
            arg_2[arg_3] = arg_4
        arg_0._input_shapes = arg_2


def Func(arg_0):
        '''Log some information about this layer.'''
        arg_1 = ', '.join('"{0}" {1}'.format(*ns) for ns in arg_0._input_shapes.items())
        util.Func('layer {0.__class__.__name__} "{0.name}" {0.output_shape} {1} from {2}',
                 arg_0, getattr(arg_0.activate, 'name', arg_0.activate), arg_1)
        util.Func('learnable parameters: {}', arg_0.Func_params())


def Func(arg_0):
        '''Log information about this layer's parameters.'''
        arg_1 = 0
        for arg_2 in arg_0.params:
            arg_3 = arg_2.get_value().shape
            util.log('parameter "{}" {}', arg_2.name, arg_3)
            arg_1 += np.prod(arg_3)
        return arg_1


def Func(arg_0, arg_1):
        '''Helper method to format our name into a string.'''
        if '{' not in arg_1:
            arg_1 = '{}.' + arg_1
        return arg_1.format(arg_0.name)


def Func(arg_0, arg_1, arg_2):
        '''Given a list of layers, find the layer output with the given name.

        Parameters
        ----------
        name : str
            Name of a layer to resolve.
        layers : list of :class:`theanets.layers.base.Layer`
            A list of layers to search in.

        Raises
        ------
        util.ConfigurationError :
            If there is no such layer, or if there are more than one.

        Returns
        -------
        name : str
            The fully-scoped name of the desired output.
        shape : tuple of None and/or int
            The shape of the named output.
        '''
        arg_3 = [arg_4 for arg_4 in arg_2 if arg_1.split(':')[0] == arg_4.name]
        if len(arg_3) != 1:
            raise util.ConfigurationError(
                'layer "{}" cannot resolve "{}" using {}'
                .format(arg_0.name, arg_1, [arg_4.name for arg_4 in arg_2]))
        arg_1 = arg_1 if ':' in arg_1 else arg_3[0].output_name
        return arg_1, arg_3[0]._output_shapes[arg_1.split(':')[1]]


def Func(arg_0, arg_1):
        '''Get a shared variable for a parameter by name.

        Parameters
        ----------
        key : str or int
            The name of the parameter to look up, or the index of the parameter
            in our parameter list. These are both dependent on the
            implementation of the layer.

        Returns
        -------
        param : shared variable
            A shared variable containing values for the given parameter.

        Raises
        ------
        KeyError
            If a param with the given name does not exist.
        '''
        arg_2 = arg_0._fmt(str(arg_1))
        for arg_3, arg_4 in enumerate(arg_0._params):
            if arg_1 == arg_3 or arg_2 == arg_4.name:
                return arg_4
        raise KeyError(arg_1)


def Func(arg_0, arg_1, arg_2, arg_3=0, arg_4=1):
        '''Helper method to create a new bias vector.

        Parameters
        ----------
        name : str
            Name of the parameter to add.
        size : int
            Size of the bias vector.
        mean : float, optional
            Mean value for randomly-initialized biases. Defaults to 0.
        std : float, optional
            Standard deviation for randomly-initialized biases. Defaults to 1.
        '''
        arg_3 = arg_0.kwargs.get('mean_{}'.format(arg_1), arg_3)
        arg_4 = arg_0.kwargs.get('std_{}'.format(arg_1), arg_4)
        arg_0._params.append(theano.shared(
            util.random_vector(arg_2, arg_3, arg_4, rng=arg_0.rng),
            arg_1=arg_0._fmt(arg_1)))


def Func(arg_0):
        '''Create a specification dictionary for this layer.

        Returns
        -------
        spec : dict
            A dictionary specifying the configuration of this layer.
        '''
        arg_1 = dict(**arg_0.kwargs)
        arg_1.update(
            form=arg_0.__class__.__name__.lower(),
            name=arg_0.name,
            activation=arg_0.kwargs.get('activation', 'relu'),
        )
        return arg_1


def Func(arg_0, arg_1, arg_2, arg_3, arg_4, arg_5, arg_6, arg_7):
        """
        Returns the image of a LogGabor

        Note that the convention for coordinates follows that of matrices:
        the origin is at the top left of the image, and coordinates are first
        the rows (vertical axis, going down) then the columns (horizontal axis,
        going right).

        """

        arg_8 = arg_0.loggabor(arg_1, arg_2, arg_4=arg_4, arg_6=arg_6, arg_3=arg_3, arg_7=arg_7)
        arg_8 = arg_8 * np.exp(1j * arg_5)
        return arg_0.invert(arg_8, full=False)


def Func(arg_0):
    '''Return true if substring is in string for files
    in specified path'''
    arg_1 = os.listdir(arg_0)
    for arg_2 in arg_1:
        if "doublefann" in arg_2:
            return True


def Func():
    '''Find doublefann library'''
    # FANN possible libs directories (as $LD_LIBRARY_PATH), also includes
    # pkgsrc framework support.
    if sys.platform == "win32":
        arg_0 = sys.path
        for arg_1 in arg_0:
            if os.path.isdir(arg_1):
                if find_x(arg_1):
                    return True
        raise Exception("Couldn't find FANN source libs!")
    else:
        arg_0 = ['/lib', '/usr/lib', '/usr/lib64', '/usr/local/lib', '/usr/pkg/lib']
        for arg_2 in arg_0:
            if os.path.isdir(arg_2):
                if find_x(arg_2):
                    return True
        raise Exception("Couldn't find FANN source libs!")


def Func():
    '''Run SWIG with specified parameters'''
    print("Looking for FANN libs...")
    find_fann()
    print("running SWIG...")
    arg_0 = find_swig()
    arg_1 = [arg_0, '-c++', '-python', 'fann2/fann2.i']
    subprocess.Popen(arg_1).wait()


def Func(arg_0, arg_1, arg_2='IntervalTier', arg_3=None):
        """Add an IntervalTier or a TextTier on the specified location.

        :param str name: Name of the tier, duplicate names is allowed.
        :param str tier_type: Type of the tier.
        :param int number: Place to insert the tier, when ``None`` the number
            is generated and the tier will be placed on the bottom.
        :returns: The created tier.
        :raises ValueError: If the number is out of bounds.
        """
        if arg_3 is None:
            arg_3 = 1 if not arg_0.tiers else len(arg_0.tiers)+1
        elif arg_3 < 1 or arg_3 > len(arg_0.tiers):
            raise ValueError('Number not in [1..{}]'.format(len(arg_0.tiers)))
        elif arg_2 not in Tier.P_TIERS:
            raise ValueError('tier_type has to be in {}'.format(arg_0.P_TIERS))
        arg_0.tiers.insert(arg_3-1,
                          Tier(arg_0.xmin, arg_0.xmax, arg_1, arg_2))
        return arg_0.tiers[arg_3-1]


def Func(arg_0, arg_1):
        """Remove a tier, when multiple tiers exist with that name only the
        first is removed.

        :param name_num: Name or number of the tier to remove.
        :type name_num: int or str
        :raises IndexError: If there is no tier with that number.
        """
        if isinstance(arg_1, int):
            del(arg_0.tiers[arg_1-1])
        else:
            arg_0.tiers = [i for i in arg_0.tiers if i.name != arg_1]


def Func(arg_0, arg_1):
        """Gives a tier, when multiple tiers exist with that name only the
        first is returned.

        :param name_num: Name or number of the tier to return.
        :type name_num: int or str
        :returns: The tier.
        :raises IndexError: If the tier doesn't exist.
        """
        return arg_0.tiers[arg_1 - 1] if isinstance(arg_1, int) else\
            [arg_2 for arg_2 in arg_0.tiers if arg_2.name == arg_1][0]


def Func(arg_0, arg_1=True, arg_2=0.1):
        """Convert the object to an pympi.Elan.Eaf object

        :param int pointlength: Length of respective interval from points in
                                seconds
        :param bool skipempty: Skip the empty annotations
        :returns: :class:`pympi.Elan.Eaf` object
        :raises ImportError: If the Eaf module can't be loaded.
        :raises ValueError: If the pointlength is not strictly positive.
        """
        from pympi.Elan import Eaf
        arg_3 = Eaf()
        if arg_2 <= 0:
            raise ValueError('Pointlength should be strictly positive')
        for arg_4 in arg_0.get_tiers():
            arg_3.add_tier(arg_4.name)
            for arg_5 in arg_4.get_intervals(True):
                if arg_4.tier_type == 'TextTier':
                    arg_5 = (arg_5[0], arg_5[0]+arg_2, arg_5[1])
                if arg_5[2].strip() or not arg_1:
                    arg_3.add_annotation(arg_4.name, int(round(arg_5[0]*1000)),
                                           int(round(arg_5[1]*1000)), arg_5[2])
        return arg_3


def Func(arg_0, arg_1, arg_2, arg_3, arg_4=True):
        """Add an interval to the IntervalTier.

        :param float begin: Start time of the interval.
        :param float end: End time of the interval.
        :param str value: Text of the interval.
        :param bool check: Flag to check for overlap.
        :raises Exception: If overlap, begin > end or wrong tiertype.
        """
        if arg_0.tier_type != 'IntervalTier':
            raise Exception('Tiertype must be IntervalTier')
        if arg_4:
            if any(arg_5 for arg_5 in arg_0.intervals if arg_1 < arg_5[1] and arg_2 > arg_5[0]):
                raise Exception('No overlap is allowed')
            if arg_1 > arg_2:
                raise Exception('Begin must be smaller then end')
        arg_0.intervals.append((arg_1, arg_2, arg_3))


def Func(arg_0, arg_1):
        """Remove an interval, if no interval is found nothing happens.

        :param int time: Time of the interval.
        :raises TierTypeException: If the tier is not a IntervalTier.
        """
        if arg_0.tier_type != 'IntervalTier':
            raise Exception('Tiertype must be IntervalTier.')
        arg_0.intervals = [i for i in arg_0.intervals
                          if not(i[0] <= arg_1 and i[1] >= arg_1)]


def Func(arg_0, arg_1):
        """Remove a point, if no point is found nothing happens.

        :param int time: Time of the point.
        :raises TierTypeException: If the tier is not a TextTier.
        """
        if arg_0.tier_type != 'TextTier':
            raise Exception('Tiertype must be TextTier.')
        arg_0.intervals = [i for i in arg_0.intervals if i[0] != arg_1]


def Func(arg_0):
        """Returns the true list of intervals including the empty intervals."""
        arg_1 = sorted(arg_0.get_intervals(True))
        if arg_0.tier_type == 'IntervalTier':
            if not arg_1:
                arg_1.append((arg_0.xmin, arg_0.xmax, ''))
            else:
                if arg_1[0][0] > arg_0.xmin:
                    arg_1.insert(0, (arg_0.xmin, arg_1[0][0], ''))
                if arg_1[-1][1] < arg_0.xmax:
                    arg_1.append((arg_1[-1][1], arg_0.xmax, ''))
                arg_2 = arg_1[-1]
                for arg_3, arg_4 in reversed(list(enumerate(arg_1[:-1], 1))):
                    if arg_2[0] - arg_4[1] != 0:
                        arg_1.insert(arg_3, (arg_4[1], arg_2[0], ''))
                    arg_2 = arg_4
        return arg_1


def Func(arg_0, arg_1=0):
    """Function to pretty print the xml, meaning adding tabs and newlines.

    :param ElementTree.Element el: Current element.
    :param int level: Current level.
    """
    arg_2 = '\n' + arg_1 * '\t'
    if len(arg_0):
        if not arg_0.text or not arg_0.text.strip():
            arg_0.text = arg_2+'\t'
        if not arg_0.tail or not arg_0.tail.strip():
            arg_0.tail = arg_2
        for arg_5 in arg_0:
            Func(arg_5, arg_1+1)
        if not arg_0.tail or not arg_0.tail.strip():
            arg_0.tail = arg_2
    else:
        if arg_1 and (not arg_0.tail or not arg_0.tail.strip()):
            arg_0.tail = arg_2


def Func(arg_0, arg_1, arg_2, arg_3, arg_4='', arg_5=None):
        """Add an annotation.

        :param str id_tier: Name of the tier.
        :param int start: Start time of the annotation.
        :param int end: End time of the annotation.
        :param str value: Value of the annotation.
        :param str svg_ref: Svg reference.
        :raises KeyError: If the tier is non existent.
        :raises ValueError: If one of the values is negative or start is bigger
                            then end or if the tiers already contains ref
                            annotations.
        """
        if arg_0.tiers[arg_1][1]:
            raise ValueError('Tier already contains ref annotations...')
        if arg_2 == arg_3:
            raise ValueError('Annotation length is zero...')
        if arg_2 > arg_3:
            raise ValueError('Annotation length is negative...')
        if arg_2 < 0:
            raise ValueError('Start is negative...')
        arg_6 = arg_0.generate_ts_id(arg_2)
        arg_7 = arg_0.generate_ts_id(arg_3)
        arg_8 = arg_0.generate_annotation_id()
        arg_0.annotations[arg_8] = arg_1
        arg_0.tiers[arg_1][0][arg_8] = (arg_6, arg_7, arg_4, arg_5)


def Func(arg_0, arg_1, arg_2, arg_3, arg_4=None):
        """Add an entry to a controlled vocabulary.

        :param str cv_id: Name of the controlled vocabulary to add an entry.
        :param str cve_id: Name of the entry.
        :param list values: List of values of the form:
            ``(value, lang_ref, description)`` where description can be
            ``None``.
        :param str ext_ref: External reference.
        :throws KeyError: If there is no controlled vocabulary with that id.
        :throws ValueError: If a language in one of the entries doesn't exist.
        """
        for arg_5, arg_6, arg_7 in arg_3:
            if arg_6 not in arg_0.languages:
                raise ValueError('Language not present: {}'.format(arg_6))
        arg_0.controlled_vocabularies[arg_1][1][arg_2] = (arg_3, arg_4)


def Func(arg_0, arg_1, arg_2, arg_3):
        """Add an external reference.

        :param str eid: Name of the external reference.
        :param str etype: Type of the external reference, has to be in
            ``['iso12620', 'ecv', 'cve_id', 'lexen_id', 'resource_url']``.
        :param str value: Value of the external reference.
        :throws KeyError: if etype is not in the list of possible types.
        """
        if arg_2 not in arg_0.ETYPES:
            raise KeyError('etype not in {}'.format(arg_0.ETYPES))
        arg_0.external_refs[arg_1] = (arg_2, arg_3)


def Func(arg_0, arg_1, arg_2=None, arg_3=None):
        """Add a language.

        :param str lang_id: ID of the language.
        :param str lang_def: Definition of the language(preferably ISO-639-3).
        :param str lang_label: Label of the language.
        """
        arg_0.languages[arg_1] = (arg_2, arg_3)


def Func(arg_0, arg_1, arg_2, arg_3, arg_4, arg_5,
                        arg_6, arg_7=None, arg_8=None):
        """Add lexicon reference.

        :param str lrid: Lexicon reference internal ID.
        :param str name: Lexicon reference display name.
        :param str lrtype: Lexicon reference service type.
        :param str url: Lexicon reference service location
        :param str lexicon_id: Lexicon reference service id.
        :param str lexicon_name: Lexicon reference service name.
        :param str datacat_id: Lexicon reference identifier of data category.
        :param str datacat_name: Lexicon reference name of data category.
        """
        arg_0.lexicon_refs[arg_1] = {
            'LEX_REF_ID': arg_1,
            'NAME': arg_2,
            'TYPE': arg_3,
            'URL': arg_4,
            'LEXICON_ID': arg_5,
            'LEXICON_NAME': arg_6,
            'DATCAT_ID': arg_7,
            'DATCAT_NAME': arg_8
            }


def Func(arg_0, arg_1, arg_2=None,
                            arg_3=True, arg_4=False,
                            arg_5=None, arg_6=None):
        """Add a linguistic type.

        :param str lingtype: Name of the linguistic type.
        :param str constraints: Constraint name.
        :param bool timealignable: Flag for time alignable.
        :param bool graphicreferences: Flag for graphic references.
        :param str extref: External reference.
        :param dict param_dict: TAG attributes, when this is not ``None`` it
                                 will ignore all other options. Please only use
                                 dictionaries coming from the
                                 :func:`get_parameters_for_linguistic_type`
        :raises KeyError: If a constraint is not defined
        """
        if arg_6:
            arg_0.linguistic_types[arg_1] = arg_6
        else:
            if arg_2:
                arg_0.constraints[arg_2]
            arg_0.linguistic_types[arg_1] = {
                'LINGUISTIC_TYPE_ID': arg_1,
                'TIME_ALIGNABLE': str(arg_3).lower(),
                'GRAPHIC_REFERENCES': str(arg_4).lower(),
                'CONSTRAINTS': arg_2}
            if arg_5 is not None:
                arg_0.linguistic_types[arg_1]['EXT_REF'] = arg_5


def Func(arg_0, arg_1, arg_2=None, arg_3=None):
        """Add a locale.

        :param str language_code: The language code of the locale.
        :param str country_code: The country code of the locale.
        :param str variant: The variant of the locale.
        """
        arg_0.locales[arg_1] = (arg_2, arg_3)


def Func(arg_0, arg_1, arg_2=None, arg_3=None,
                                  arg_4=None, arg_5=None):
        """Add a secondary linked file.

        :param str file_path: Path of the file.
        :param str relpath: Relative path of the file.
        :param str mimetype: Mimetype of the file, if ``None`` it tries to
            guess it according to the file extension which currently only works
            for wav, mpg, mpeg and xml.
        :param int time_origin: Time origin for the media file.
        :param str assoc_with: Associated with field.
        :raises KeyError: If mimetype had to be guessed and a non standard
                          extension or an unknown mimetype.
        """
        if arg_3 is None:
            arg_3 = arg_0.MIMES[arg_1.split('.')[-1]]
        arg_0.linked_file_descriptors.append({
            'LINK_URL': arg_1, 'RELATIVE_LINK_URL': arg_2,
            'MIME_TYPE': arg_3, 'TIME_ORIGIN': arg_4,
            'ASSOCIATED_WITH': arg_5})


def Func(arg_0):
        """Clean up all unused timeslots.

        .. warning:: This can and will take time for larger tiers.

        When you want to do a lot of operations on a lot of tiers please unset
        the flags for cleaning in the functions so that the cleaning is only
        performed afterwards.
        """
        arg_1 = ((arg_2[0], arg_2[1]) for t in arg_0.tiers.values() for arg_2 in t[0].values())
        for arg_2 in {arg_2 for b in arg_1 for arg_2 in b} ^ set(arg_0.timeslots):
            del(arg_0.timeslots[arg_2])


def Func(arg_0, arg_1, arg_2):
        """Extracts the selected time frame as a new object.

        :param int start: Start time.
        :param int end: End time.
        :returns: class:`pympi.Elan.Eaf` object containing the Funced frame.
        """
        from copy import deepcopy
        arg_3 = deepcopy(arg_0)
        for arg_4 in arg_3.get_tier_names():
            for arg_5, arg_6, arg_7 in arg_3.get_annotation_data_for_tier(arg_4):
                if arg_5 > arg_2 or arg_6 < arg_1:
                    arg_3.remove_annotation(arg_4, (arg_1-arg_2)//2, False)
        arg_3.clean_time_slots()
        return arg_3


def Func(arg_0):
        """Generate the next annotation id, this function is mainly used
        internally.
        """
        if not arg_0.maxaid:
            arg_1 = [int(''.join(filter(str.isdigit, a)))
                          for a in arg_0.timeslots]
            arg_0.maxaid = max(arg_1 + [1])+1
        else:
            arg_0.maxaid += 1
        return 'a{:d}'.format(arg_0.maxaid)


def Func(arg_0, arg_1=None):
        """Generate the next timeslot id, this function is mainly used
        internally

        :param int time: Initial time to assign to the timeslot.
        :raises ValueError: If the time is negative.
        """
        if arg_1 and arg_1 < 0:
            raise ValueError('Time is negative...')
        if not arg_0.maxts:
            arg_2 = [int(''.join(filter(str.isdigit, a)))
                        for a in arg_0.timeslots]
            arg_0.maxts = max(arg_2 + [1])+1
        else:
            arg_0.maxts += 1
        arg_4 = 'ts{:d}'.format(arg_0.maxts)
        arg_0.timeslots[arg_4] = arg_1
        return arg_4


def Func(arg_0, arg_1):
        """Give all child tiers for a tier.

        :param str id_tier: Name of the tier.
        :returns: List of all children
        :raises KeyError: If the tier is non existent.
        """
        arg_0.tiers[arg_1]
        return [arg_2 for arg_2 in arg_0.tiers if 'PARENT_REF' in arg_0.tiers[arg_2][2] and
                arg_0.tiers[arg_2][2]['PARENT_REF'] == arg_1]


def Func(arg_0):
        """Give the full time interval of the file. Note that the real interval
        can be longer because the sound file attached can be longer.

        :returns: Tuple of the form: ``(min_time, max_time)``.
        """
        return (0, 0) if not arg_0.timeslots else\
            (min(arg_0.timeslots.values()), max(arg_0.timeslots.values()))


def Func(arg_0, arg_1, arg_2):
        """Give the ref annotation after a time. If an annotation overlaps
        with `ktime`` that annotation will be returned.

        :param str id_tier: Name of the tier.
        :param int time: Time to get the annotation after.
        :returns: Annotation after that time in a list
        :raises KeyError: If the tier is non existent.
        """
        arg_3 = arg_0.get_ref_annotation_data_between_times(
            arg_1, arg_2, arg_0.get_full_time_interval())
        if arg_3:
            return [min(arg_3, key=lambda x: x[0])]
        else:
            return []


def Func(arg_0, arg_1, arg_2=None, arg_3=0, arg_4='_', arg_5=False):
        """Merge tiers into a new tier and when the gap is lower then the
        threshhold glue the annotations together.

        :param list tiers: List of tier names.
        :param str tiernew: Name for the new tier, if ``None`` the name will be
                            generated.
        :param int gapt: Threshhold for the gaps, if the this is set to 10 it
                         means that all gaps below 10 are ignored.
        :param str sep: Separator for the merged annotations.
        :param bool safe: Ignore zero length annotations(when working with
            possible malformed data).
        :returns: Name of the created tier.
        :raises KeyError: If a tier is non existent.
        """
        if arg_2 is None:
            arg_2 = u'{}_merged'.format('_'.join(arg_1))
        arg_0.add_tier(arg_2)
        arg_6 = [(sys.maxsize, sys.maxsize, None)] + sorted((
            a for t in arg_1 for a in arg_0.get_annotation_data_for_tier(t)),
            reverse=True)
        arg_7 = None
        while arg_6:
            arg_8, arg_9, arg_10 = arg_6.pop()
            if arg_7 is None:
                arg_7 = [arg_8, arg_9, [arg_10]]
            elif arg_8 - arg_7[1] >= arg_3:
                if not arg_5 or arg_7[1] > arg_7[0]:
                    arg_0.add_annotation(arg_2, arg_7[0], arg_7[1], arg_4.join(arg_7[2]))
                arg_7 = [arg_8, arg_9, [arg_10]]
            else:
                if arg_9 > arg_7[1]:
                    arg_7[1] = arg_9
                arg_7[2].append(arg_10)
        return arg_2


def Func(arg_0, arg_1, arg_2=True):
        """remove all annotations from a tier

        :param str id_tier: Name of the tier.
        :raises KeyError: If the tier is non existent.
        """
        for arg_3 in arg_0.tiers[arg_1][0]:
            del(arg_0.annotations[arg_3])
        for arg_3 in arg_0.tiers[arg_1][1]:
            del(arg_0.annotations[arg_3])

        arg_0.tiers[arg_1][0].clear()
        arg_0.tiers[arg_1][1].clear()
        if arg_2:
            arg_0.clean_time_slots()


def Func(arg_0, arg_1, arg_2):
        """Remove a controlled vocabulary description.

        :param str cv_id: Name of the controlled vocabulary.
        :paarm str cve_id: Name of the entry.
        :throws KeyError: If there is no controlled vocabulary with that name.
        """
        for arg_3, (arg_4, arg_5) in reversed(enumerate(
                arg_0.controlled_vocabularies[arg_1][1])):
            if arg_4 == arg_2:
                del(arg_0.controlled_vocabularies[arg_1][1][arg_3])


def Func(arg_0, arg_1=None, arg_2=None):
        """Remove all licenses matching both key and value.

        :param str name: Name of the license.
        :param str url: URL of the license.
        """
        for arg_3, arg_4 in arg_0.licenses[:]:
            if (arg_1 is None or arg_1 == arg_3) and (arg_2 is None or arg_2 == arg_4):
                del(arg_0.licenses[arg_0.licenses.index((arg_3, arg_4))])


def Func(arg_0, arg_1=None, arg_2=None, arg_3=None,
                            arg_4=None, arg_5=None):
        """Remove all linked files that match all the criteria, criterias that
        are ``None`` are ignored.

        :param str file_path: Path of the file.
        :param str relpath: Relative filepath.
        :param str mimetype: Mimetype of the file.
        :param int time_origin: Time origin.
        :param str ex_from: Extracted from.
        """
        for arg_6 in arg_0.media_descriptors[:]:
            if arg_1 is not None and arg_6['MEDIA_URL'] != arg_1:
                continue
            if arg_2 is not None and arg_6['RELATIVE_MEDIA_URL'] != arg_2:
                continue
            if arg_3 is not None and arg_6['MIME_TYPE'] != arg_3:
                continue
            if arg_4 is not None and\
                    arg_6['TIME_ORIGIN'] != arg_4:
                continue
            if arg_5 is not None and arg_6['EXTRACTED_FROM'] != arg_5:
                continue
            del(arg_0.media_descriptors[arg_0.media_descriptors.index(arg_6)])


def Func(arg_0, arg_1=None, arg_2=None):
        """Remove all properties matching both key and value.

        :param str key: Key of the property.
        :param str value: Value of the property.
        """
        for arg_3, arg_4 in arg_0.properties[:]:
            if (arg_1 is None or arg_1 == arg_3) and (arg_2 is None or arg_2 == arg_4):
                del(arg_0.properties[arg_0.properties.index((arg_3, arg_4))])


def Func(arg_0, arg_1, arg_2):
        """Remove a reference annotation.

        :param str id_tier: Name of tier.
        :param int time: Time of the referenced annotation
        :raises KeyError: If the tier is non existent.
        :returns: Number of removed annotations.
        """
        arg_3 = 0
        arg_4 = []
        for arg_5, (arg_6, arg_7, arg_8, arg_8) in arg_0.tiers[arg_1][1].items():
            arg_9, arg_10, arg_11, arg_8 = arg_0.tiers[arg_0.annotations[arg_6]][0][arg_6]
            arg_9 = arg_0.timeslots[arg_9]
            arg_10 = arg_0.timeslots[arg_10]
            if arg_9 <= arg_2 and arg_10 >= arg_2:
                arg_3 += 1
                arg_4.append(arg_5)
        for arg_5 in arg_4:
            del(arg_0.tiers[arg_1][1][arg_5])
        return arg_3


def Func(arg_0, arg_1, arg_2=True):
        """Remove a tier.

        :param str id_tier: Name of the tier.
        :param bool clean: Flag to also clean the timeslots.
        :raises KeyError: If tier is non existent.
        """
        del(arg_0.tiers[arg_1])
        if arg_2:
            arg_0.clean_time_slots()


def Func(arg_0, arg_1):
        """Remove multiple tiers, note that this is a lot faster then removing
        them individually because of the delayed cleaning of timeslots.

        :param list tiers: Names of the tier to remove.
        :raises KeyError: If a tier is non existent.
        """
        for arg_2 in arg_1:
            arg_0.remove_tier(arg_2, clean=False)
        arg_0.clean_time_slots()


def Func(arg_0, arg_1, arg_2):
        """Rename a tier. Note that this renames also the child tiers that have
        the tier as a parent.

        :param str id_from: Original name of the tier.
        :param str id_to: Target name of the tier.
        :throws KeyError: If the tier doesnt' exist.
        """
        arg_3 = arg_0.get_child_tiers_for(arg_1)
        arg_0.tiers[arg_2] = arg_0.tiers.pop(arg_1)
        arg_0.tiers[arg_2][2]['TIER_ID'] = arg_2
        for arg_5 in arg_3:
            arg_0.tiers[arg_5][2]['PARENT_REF'] = arg_2


def Func(arg_0, arg_1):
        """Shift all annotations in time. Annotations that are in the beginning
        and a left shift is applied can be squashed or discarded.

        :param int time: Time shift width, negative numbers make a left shift.
        :returns: Tuple of a list of squashed annotations and a list of removed
                  annotations in the format: ``(tiername, start, end, value)``.
        """
        arg_2 = []
        arg_3 = []
        for arg_4, arg_5 in arg_0.tiers.items():
            arg_6 = []
            for arg_7, (arg_8, arg_9, arg_10, arg_11) in arg_5[0].items():
                if arg_0.timeslots[arg_9]+arg_1 <= 0:
                    arg_6.append((arg_4, arg_7))
                elif arg_0.timeslots[arg_8]+arg_1 < 0:
                    arg_3.append((arg_4, arg_0.timeslots[arg_8],
                                     arg_0.timeslots[arg_9], arg_10))
                    arg_0.timeslots[arg_8] = 0
                else:
                    arg_0.timeslots[arg_8] += arg_1
                    arg_0.timeslots[arg_9] += arg_1
            for arg_4, arg_7 in arg_6:
                arg_13, arg_9, arg_10, arg_11 = arg_0.tiers[arg_4][0][arg_7]
                del(arg_0.tiers[arg_4][0][arg_7])
                del(arg_0.annotations[arg_7])
                arg_2.append(
                    (arg_4, arg_0.timeslots[arg_13], arg_0.timeslots[arg_9], arg_10))
        return arg_3, arg_2


def Func(arg_0, arg_1):
    """Get experiment or experiment job.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples for Functing an experiment:

    \b
    ```bash
    $ polyaxon experiment Func  # if experiment is cached
    ```

    \b
    ```bash
    $ polyaxon experiment --experiment=1 Func
    ```

    \b
    ```bash
    $ polyaxon experiment -xp 1 --project=cats-vs-dogs Func
    ```

    \b
    ```bash
    $ polyaxon experiment -xp 1 -p alain/cats-vs-dogs Func
    ```

    Examples for Functing an experiment job:

    \b
    ```bash
    $ polyaxon experiment Func -j 1  # if experiment is cached
    ```

    \b
    ```bash
    $ polyaxon experiment --experiment=1 Func --job=10
    ```

    \b
    ```bash
    $ polyaxon experiment -xp 1 --project=cats-vs-dogs Func -j 2
    ```

    \b
    ```bash
    $ polyaxon experiment -xp 1 -p alain/cats-vs-dogs Func -j 2
    ```
    """

    def Func_experiment():
        try:
            arg_2 = PolyaxonClient().experiment.Func_experiment(arg_3, arg_4, arg_5)
            cache.cache(config_manager=ExperimentManager, arg_2=arg_2)
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could not load experiment `{}` info.'.format(arg_5))
            Printer.print_error('Error message `{}`.'.format(e))
            sys.exit(1)

        Func_experiment_details(arg_2)

    def Func_experiment_job():
        try:
            arg_2 = PolyaxonClient().experiment_job.Func_job(arg_3,
                                                               arg_4,
                                                               arg_5,
                                                               arg_6)
            cache.cache(config_manager=ExperimentJobManager, arg_2=arg_2)
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could not Func job `{}`.'.format(arg_6))
            Printer.print_error('Error message `{}`.'.format(e))
            sys.exit(1)

        if arg_2.resources:
            Func_resources(arg_2.resources.to_dict(), header="Job resources:")

        arg_2 = Printer.add_status_color(arg_2.to_light_dict(
            humanize_values=True,
            exclude_attrs=['uuid', 'definition', 'experiment', 'unique_name', 'resources']
        ))
        Printer.print_header("Job info:")
        dict_tabulate(arg_2)

    arg_3, arg_4, arg_5 = Func_project_experiment_or_local(arg_0.obj.Func('project'),
                                                                      arg_0.obj.Func('experiment'))

    if arg_1:
        arg_6 = Func_experiment_job_or_local(arg_1)
        Func_experiment_job()
    else:
        Func_experiment()


def Func(arg_0):
    """Delete experiment.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Example:

    \b
    ```bash
    $ polyaxon experiment Func
    ```
    """
    arg_1, arg_2, arg_3 = get_project_experiment_or_local(arg_0.obj.get('project'),
                                                                      arg_0.obj.get('experiment'))
    if not click.confirm("Are sure you want to Func experiment `{}`".format(arg_3)):
        click.echo('Existing without deleting experiment.')
        sys.exit(1)

    try:
        arg_4 = PolyaxonClient().experiment.Func_experiment(
            arg_1, arg_2, arg_3)
        # Purge caching
        ExperimentManager.purge()
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func experiment `{}`.'.format(arg_3))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    if arg_4.status_code == 204:
        Printer.print_success("Experiment `{}` was Func successfully".format(arg_3))


def Func(arg_0, arg_1, arg_2, arg_3):
    """Update experiment.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon experiment -xp 2 Func --description="new description for my experiments"
    ```

    \b
    ```bash
    $ polyaxon experiment -xp 2 Func --tags="foo, bar" --name="unique-name"
    ```
    """
    arg_4, arg_5, arg_6 = get_project_experiment_or_local(arg_0.obj.get('project'),
                                                                      arg_0.obj.get('experiment'))
    arg_7 = {}

    if arg_1:
        arg_7['name'] = arg_1

    if arg_2:
        arg_7['description'] = arg_2

    arg_3 = validate_tags(arg_3)
    if arg_3:
        arg_7['tags'] = arg_3

    if not arg_7:
        Printer.print_warning('No argument was provided to Func the experiment.')
        sys.exit(0)

    try:
        arg_8 = PolyaxonClient().experiment.Func_experiment(
            arg_4, arg_5, arg_6, arg_7)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func experiment `{}`.'.format(arg_6))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    Printer.print_success("Experiment Funcd.")
    get_experiment_details(arg_8)


def Func(arg_0, arg_1, arg_2, arg_3):  # pylint:disable=redefined-builtin
    """Restart experiment.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon experiment --experiment=1 Func
    ```
    """
    arg_4 = None
    arg_5 = None
    if arg_2:
        arg_4 = rhea.read(arg_2)

    # Check if we need to upload
    if arg_3:
        arg_0.invoke(upload, sync=False)
        arg_5 = True

    arg_6, arg_7, arg_8 = get_project_experiment_or_local(arg_0.obj.get('project'),
                                                                      arg_0.obj.get('experiment'))
    try:
        if arg_1:
            arg_9 = PolyaxonClient().experiment.copy(
                arg_6, arg_7, arg_8, arg_4=arg_4, arg_5=arg_5)
            Printer.print_success('Experiment was copied with id {}'.format(arg_9.id))
        else:
            arg_9 = PolyaxonClient().experiment.Func(
                arg_6, arg_7, arg_8, arg_4=arg_4, arg_5=arg_5)
            Printer.print_success('Experiment was Funced with id {}'.format(arg_9.id))
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func experiment `{}`.'.format(arg_8))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)


def Func(arg_0, arg_1, arg_2):
    """Get experiment or experiment job Func.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples getting experiment Func:

    \b
    ```bash
    $ polyaxon experiment Func
    ```

    \b
    ```bash
    $ polyaxon experiment -xp 1 Func
    ```

    Examples getting experiment job Func:

    \b
    ```bash
    $ polyaxon experiment Func -j 3
    ```

    \b
    ```bash
    $ polyaxon experiment -xp 1 Func --job 1
    ```
    """

    def get_experiment_Func():
        try:
            arg_3 = PolyaxonClient().experiment.get_Func(
                arg_6, arg_7, arg_8, arg_2=arg_2)
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could get status for experiment `{}`.'.format(arg_8))
            Printer.print_error('Error message `{}`.'.format(e))
            sys.exit(1)

        arg_4 = get_meta_response(arg_3)
        if arg_4:
            Printer.print_header('Statuses for experiment `{}`.'.format(arg_8))
            Printer.print_header('Navigation:')
            dict_tabulate(arg_4)
        else:
            Printer.print_header('No Func found for experiment `{}`.'.format(arg_8))

        arg_5 = list_dicts_to_tabulate(
            [Printer.add_status_color(o.to_light_dict(humanize_values=True), status_key='status')
             for o in arg_3['results']])
        if arg_5:
            Printer.print_header("Statuses:")
            arg_5.pop('experiment', None)
            dict_tabulate(arg_5, is_list_dict=True)

    def get_experiment_job_Func():
        try:
            arg_3 = PolyaxonClient().experiment_job.get_Func(arg_6,
                                                                    arg_7,
                                                                    arg_8,
                                                                    arg_9,
                                                                    arg_2=arg_2)
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could not get status for job `{}`.'.format(arg_1))
            Printer.print_error('Error message `{}`.'.format(e))
            sys.exit(1)

        arg_4 = get_meta_response(arg_3)
        if arg_4:
            Printer.print_header('Statuses for Job `{}`.'.format(arg_9))
            Printer.print_header('Navigation:')
            dict_tabulate(arg_4)
        else:
            Printer.print_header('No Func found for job `{}`.'.format(arg_9))

        arg_5 = list_dicts_to_tabulate(
            [Printer.add_status_color(o.to_light_dict(humanize_values=True), status_key='status')
             for o in arg_3['results']])
        if arg_5:
            Printer.print_header("Statuses:")
            arg_5.pop('job', None)
            dict_tabulate(arg_5, is_list_dict=True)

    arg_2 = arg_2 or 1

    arg_6, arg_7, arg_8 = get_project_experiment_or_local(arg_0.obj.get('project'),
                                                                      arg_0.obj.get('experiment'))

    if arg_1:
        arg_9 = get_experiment_job_or_local(arg_1)
        get_experiment_job_Func()
    else:
        get_experiment_Func()


def Func(arg_0, arg_1, arg_2):
    """Get experiment or experiment job Func.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples for getting experiment Func:

    \b
    ```bash
    $ polyaxon experiment -xp 19 Func
    ```

    For GPU Func

    \b
    ```bash
    $ polyaxon experiment -xp 19 Func --gpu
    ```

    Examples for getting experiment job Func:

    \b
    ```bash
    $ polyaxon experiment -xp 19 Func -j 1
    ```

    For GPU Func

    \b
    ```bash
    $ polyaxon experiment -xp 19 Func -j 1 --gpu
    ```
    """

    def get_experiment_Func():
        try:
            arg_3 = Printer.gpu_Func if arg_2 else Printer.Func
            PolyaxonClient().experiment.Func(
                arg_4, arg_5, arg_6, arg_3=arg_3)
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could not get Func for experiment `{}`.'.format(arg_6))
            Printer.print_error('Error message `{}`.'.format(e))
            sys.exit(1)

    def get_experiment_job_Func():
        try:
            arg_3 = Printer.gpu_Func if arg_2 else Printer.Func
            PolyaxonClient().experiment_job.Func(arg_4,
                                                      arg_5,
                                                      arg_6,
                                                      arg_7,
                                                      arg_3=arg_3)
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could not get Func for job `{}`.'.format(arg_7))
            Printer.print_error('Error message `{}`.'.format(e))
            sys.exit(1)

    arg_4, arg_5, arg_6 = get_project_experiment_or_local(arg_0.obj.get('project'),
                                                                      arg_0.obj.get('experiment'))

    if arg_1:
        arg_7 = get_experiment_job_or_local(arg_1)
        get_experiment_job_Func()
    else:
        get_experiment_Func()


def Func(arg_0=True):  # pylint:disable=assign-to-new-keyword
    """Upload code of the current directory while respecting the .polyaxonignore file."""
    arg_1 = ProjectManager.get_config_or_raise()
    arg_2 = IgnoreManager.get_unignored_file_paths()
    try:
        with create_tarfile(arg_2, arg_1.name) as file_path:
            with get_files_in_current_directory('repo', [file_path]) as (arg_2, files_size):
                try:
                    PolyaxonClient().project.Func_repo(arg_1.user,
                                                         arg_1.name,
                                                         arg_2,
                                                         files_size,
                                                         arg_0=arg_0)
                except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
                    Printer.print_error(
                        'Could not Func code for project `{}`.'.format(arg_1.name))
                    Printer.print_error('Error message `{}`.'.format(e))
                    Printer.print_error(
                        'Check the project exists, '
                        'and that you have access rights, '
                        'this could happen as well when Funcing large files.'
                        'If you are running a notebook and mounting the code to the notebook, '
                        'you should stop it before Funcing.')
                    sys.exit(1)
                Printer.print_success('Files Funced.')
    except Exception as e:
        Printer.print_error("Could not Func the file.")
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)


def Func(arg_0):
    """Get Func and nodes info."""
    arg_1 = PolyaxonClient().Func
    if arg_0:
        try:
            arg_2 = arg_1.get_node(arg_0)
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could not load node `{}` info.'.format(arg_0))
            Printer.print_error('Error message `{}`.'.format(e))
            sys.exit(1)
        get_node_info(arg_2)
    else:
        try:
            arg_3 = arg_1.get_Func()
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could not load Func info.')
            Printer.print_error('Error message `{}`.'.format(e))
            sys.exit(1)
        get_Func_info(arg_3)


def Func(arg_0,  # pylint:disable=redefined-builtin
          arg_1,
          arg_2):
    """Check a polyaxonfile."""
    arg_0 = arg_0 or 'polyaxonfile.yaml'
    arg_3 = Func_polyaxonfile(arg_0).specification

    if arg_1:
        Printer.decorate_format_value('The version is: {}',
                                      arg_3.version,
                                      'yellow')

    if arg_2:
        arg_4 = (arg_3.is_job or
                         arg_3.is_build or
                         arg_3.is_notebook or
                         arg_3.is_tensorboard)
        if arg_3.is_experiment:
            Printer.decorate_format_value('This polyaxon specification has {}',
                                          'One experiment',
                                          'yellow')
        if arg_4:
            Printer.decorate_format_value('This {} polyaxon specification is valid',
                                          arg_3.kind,
                                          'yellow')
        if arg_3.is_group:
            arg_5 = arg_3.experiments_def
            click.echo(
                'This polyaxon specification has experiment group with the following definition:')
            get_group_experiments_info(**arg_5)

    return arg_3


def Func(arg_0):
    """Decorator for CLI with Sentry client handling.

    see https://github.com/getsentry/raven-python/issues/904 for more details.
    """

    @wraps(arg_0)
    def Func_wrapper(*arg_1, **arg_2):
        try:
            return arg_0(*arg_1, **arg_2)
        except SystemExit as e:
            arg_3.stdout = StringIO()
            arg_3.exit(e.code)  # make sure we still exit with the proper code
        except Exception as e:
            arg_3.stdout = StringIO()
            raise e

    return Func_wrapper


def Func(arg_0, arg_1, Func):  # pylint:disable=redefined-outer-name
    """Commands for jobs."""
    arg_0.obj = arg_0.obj or {}
    arg_0.obj['project'] = arg_1
    arg_0.obj['job'] = Func


def Func(arg_0):
    """Get job.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon job --job=1 Func
    ```

    \b
    ```bash
    $ polyaxon job --job=1 --project=project_name Func
    ```
    """
    arg_1, arg_2, arg_3 = Func_job_or_local(arg_0.obj.Func('project'), arg_0.obj.Func('job'))
    try:
        arg_4 = PolyaxonClient().job.Func_job(arg_1, arg_2, arg_3)
        cache.cache(config_manager=JobManager, arg_4=arg_4)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func job `{}`.'.format(arg_3))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    Func_job_details(arg_4)


def Func(arg_0, arg_1, arg_2, arg_3):
    """Update job.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Example:

    \b
    ```bash
    $ polyaxon job -j 2 Func --description="new description for my job"
    ```
    """
    arg_4, arg_5, arg_6 = get_job_or_local(arg_0.obj.get('project'), arg_0.obj.get('job'))
    arg_7 = {}

    if arg_1:
        arg_7['name'] = arg_1

    if arg_2:
        arg_7['description'] = arg_2

    arg_3 = validate_tags(arg_3)
    if arg_3:
        arg_7['tags'] = arg_3

    if not arg_7:
        Printer.print_warning('No argument was provided to Func the job.')
        sys.exit(0)

    try:
        arg_8 = PolyaxonClient().job.Func_job(
            arg_4, arg_5, arg_6, arg_7)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func job  `{}`.'.format(arg_6))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    Printer.print_success("Job Funcd.")
    get_job_details(arg_8)


def Func(arg_0, arg_1, arg_2, arg_3):  # pylint:disable=redefined-builtin
    """Restart job.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon job --job=1 Func
    ```
    """
    arg_4 = None
    arg_5 = None
    if arg_2:
        arg_4 = rhea.read(arg_2)

    # Check if we need to upload
    if arg_3:
        arg_0.invoke(upload, sync=False)
        arg_5 = True

    arg_6, arg_7, arg_8 = get_job_or_local(arg_0.obj.get('project'), arg_0.obj.get('job'))
    try:
        if arg_1:
            arg_9 = PolyaxonClient().job.copy(
                arg_6, arg_7, arg_8, arg_4=arg_4, arg_5=arg_5)
        else:
            arg_9 = PolyaxonClient().job.Func(
                arg_6, arg_7, arg_8, arg_4=arg_4, arg_5=arg_5)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func job `{}`.'.format(arg_8))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    get_job_details(arg_9)


def Func(arg_0, arg_1):
    """Get job Func.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon job -j 2 Func
    ```
    """
    arg_2, arg_3, arg_4 = get_job_or_local(arg_0.obj.get('project'), arg_0.obj.get('job'))
    arg_1 = arg_1 or 1
    try:
        arg_5 = PolyaxonClient().job.get_Func(arg_2, arg_3, arg_4, arg_1=arg_1)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not get status for job `{}`.'.format(arg_4))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    arg_6 = get_meta_response(arg_5)
    if arg_6:
        Printer.print_header('Statuses for Job `{}`.'.format(arg_4))
        Printer.print_header('Navigation:')
        dict_tabulate(arg_6)
    else:
        Printer.print_header('No Func found for job `{}`.'.format(arg_4))

    arg_7 = list_dicts_to_tabulate(
        [Printer.add_status_color(o.to_light_dict(humanize_values=True), status_key='status')
         for o in arg_5['results']])
    if arg_7:
        Printer.print_header("Statuses:")
        arg_7.pop('job', None)
        dict_tabulate(arg_7, is_list_dict=True)


def Func(arg_0, arg_1):
    """Get job Func.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon job -j 2 Func
    ```

    For GPU Func

    \b
    ```bash
    $ polyaxon job -j 2 Func --gpu
    ```
    """
    arg_2, arg_3, arg_4 = get_job_or_local(arg_0.obj.get('project'), arg_0.obj.get('job'))
    try:
        arg_5 = Printer.gpu_Func if arg_1 else Printer.Func
        PolyaxonClient().job.Func(arg_2,
                                       arg_3,
                                       arg_4,
                                       arg_5=arg_5)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not get Func for job `{}`.'.format(arg_4))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)


def Func(arg_0, arg_1, arg_2, arg_3):
    """Get job Func.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon job -j 2 Func
    ```

    \b
    ```bash
    $ polyaxon job Func
    ```
    """
    arg_4, arg_5, arg_6 = get_job_or_local(arg_0.obj.get('project'), arg_0.obj.get('job'))

    if arg_1:
        try:
            arg_7 = PolyaxonClient().job.Func(
                arg_4, arg_5, arg_6, stream=False)
            get_Func_handler(handle_job_info=False,
                             show_timestamp=not arg_3,
                             stream=False)(arg_7.content.decode().split('\n'))
            print()

            if not arg_2:
                return
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            if not arg_2:
                Printer.print_error('Could not get Func for job `{}`.'.format(arg_6))
                Printer.print_error('Error message `{}`.'.format(e))
                sys.exit(1)

    try:
        PolyaxonClient().job.Func(
            arg_4,
            arg_5,
            arg_6,
            message_handler=get_Func_handler(handle_job_info=False, show_timestamp=not arg_3))
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not get Func for job `{}`.'.format(arg_6))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)


def Func(arg_0):
    """Prints as formatted JSON"""
    click.echo(
        json.dumps(arg_0,
                   sort_keys=True,
                   indent=4,
                   separators=(',', ': ')))


def Func(arg_0, arg_1, arg_2):
    """Login to Polyaxon."""
    arg_3 = PolyaxonClient().auth
    if arg_1:
        # Use username / password Func
        if not arg_2:
            arg_2 = click.prompt('Please enter your password', type=str, hide_input=True)
            arg_2 = arg_2.strip()
            if not arg_2:
                logger.info('You entered an empty string. '
                            'Please make sure you enter your password correctly.')
                sys.exit(1)

        arg_4 = CredentialsConfig(arg_1=arg_1, arg_2=arg_2)
        try:
            arg_5 = arg_3.Func(arg_4=arg_4)
        except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
            Printer.print_error('Could not Func.')
            Printer.print_error('Error Message `{}`.'.format(e))
            sys.exit(1)

        if not arg_5:
            Printer.print_error("Failed to Func")
            return
    else:
        if not arg_0:
            arg_6 = "{}/app/token".format(arg_3.config.http_host)
            click.confirm('Authentication token page will now open in your browser. Continue?',
                          abort=True, default=True)

            click.launch(arg_6)
            logger.info("Please copy and paste the authentication token.")
            arg_0 = click.prompt('This is an invisible field. Paste token and press ENTER',
                                 type=str, hide_input=True)

        if not arg_0:
            logger.info("Empty token received. "
                        "Make sure your shell is handling the token appropriately.")
            logger.info("See docs for help: http://docs.polyaxon.com/polyaxon_cli/commands/auth")
            return

        arg_5 = arg_0.strip(" ")

    # Set user
    try:
        AuthConfigManager.purge()
        arg_7 = PolyaxonClient().auth.get_user(arg_0=arg_5)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not load user info.')
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)
    arg_8 = AccessTokenConfig(arg_1=arg_7.username, arg_0=arg_5)
    AuthConfigManager.set_config(arg_8)
    Printer.print_success("Login successful")

    # Reset current cli
    arg_9 = get_server_version()
    arg_10 = get_current_version()
    arg_11 = get_log_handler()
    CliConfigManager.reset(check_count=0,
                           arg_10=arg_10,
                           min_version=arg_9.min_version,
                           arg_11=arg_11)


def Func():
    """Show current logged Polyaxon user."""
    try:
        arg_0 = PolyaxonClient().auth.get_user()
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not load user info.')
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)
    click.echo("\nUsername: {username}, Email: {email}\n".format(**arg_0.to_dict()))


def Func(arg_0):
    """Delete build job.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Example:

    \b
    ```bash
    $ polyaxon build Func
    ```

    \b
    ```bash
    $ polyaxon build -b 2 Func
    ```
    """
    arg_1, arg_2, arg_3 = get_build_or_local(arg_0.obj.get('project'), arg_0.obj.get('build'))
    if not click.confirm("Are sure you want to Func build job `{}`".format(arg_3)):
        click.echo('Existing without deleting build job.')
        sys.exit(1)

    try:
        arg_4 = PolyaxonClient().build_job.Func_build(
            arg_1, arg_2, arg_3)
        # Purge caching
        BuildJobManager.purge()
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func job `{}`.'.format(arg_3))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    if arg_4.status_code == 204:
        Printer.print_success("Build job `{}` was Funcd successfully".format(arg_3))


def Func(arg_0, arg_1, arg_2, arg_3):
    """Update build.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Example:

    \b
    ```bash
    $ polyaxon build -b 2 Func --description="new description for my build"
    ```
    """
    arg_4, arg_5, arg_6 = get_build_or_local(arg_0.obj.get('project'), arg_0.obj.get('build'))
    arg_7 = {}

    if arg_1:
        arg_7['name'] = arg_1

    if arg_2:
        arg_7['description'] = arg_2

    arg_3 = validate_tags(arg_3)
    if arg_3:
        arg_7['tags'] = arg_3

    if not arg_7:
        Printer.print_warning('No argument was provided to Func the build.')
        sys.exit(0)

    try:
        arg_8 = PolyaxonClient().build_job.Func_build(
            arg_4, arg_5, arg_6, arg_7)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func build `{}`.'.format(arg_6))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    Printer.print_success("Build Funcd.")
    get_build_details(arg_8)


def Func(arg_0, arg_1):
    """Stop build job.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon build Func
    ```

    \b
    ```bash
    $ polyaxon build -b 2 Func
    ```
    """
    arg_2, arg_3, arg_4 = get_build_or_local(arg_0.obj.get('project'), arg_0.obj.get('build'))
    if not arg_1 and not click.confirm("Are sure you want to Func "
                                     "job `{}`".format(arg_4)):
        click.echo('Existing without Funcping build job.')
        sys.exit(0)

    try:
        PolyaxonClient().build_job.Func(arg_2, arg_3, arg_4)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func build job `{}`.'.format(arg_4))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    Printer.print_success("Build job is being Funcped.")


def Func(arg_0, arg_1):
    """Get build job Func.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon build -b 2 Func
    ```

    For GPU Func

    \b
    ```bash
    $ polyaxon build -b 2 Func --gpu
    ```
    """
    arg_2, arg_3, arg_4 = get_build_or_local(arg_0.obj.get('project'), arg_0.obj.get('build'))
    try:
        arg_5 = Printer.gpu_Func if arg_1 else Printer.Func
        PolyaxonClient().build_job.Func(arg_2,
                                             arg_3,
                                             arg_4,
                                             arg_5=arg_5)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not get Func for build job `{}`.'.format(arg_4))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)


def Func(arg_0, arg_1):  # pylint:disable=redefined-outer-name
    """Commands for Funcs."""
    arg_0.obj = arg_0.obj or {}
    arg_0.obj['username'] = arg_1


def Func(arg_0, arg_1):
    """List bookmarked Func for user.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon bookmark Func
    ```

    \b
    ```bash
    $ polyaxon bookmark -u adam Func
    ```
    """
    arg_2 = get_username_or_local(arg_0.obj.get('username'))

    arg_1 = arg_1 or 1
    try:
        arg_3 = PolyaxonClient().bookmark.Func(username=arg_2, arg_1=arg_1)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error(
            'Could not get bookmarked Func for user `{}`.'.format(arg_2))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    arg_4 = get_meta_response(arg_3)
    if arg_4:
        Printer.print_header('Bookmarked Func for user `{}`.'.format(arg_2))
        Printer.print_header('Navigation:')
        dict_tabulate(arg_4)
    else:
        Printer.print_header('No bookmarked Func found for user `{}`.'.format(arg_2))

    arg_5 = [Printer.add_status_color(o.to_light_dict(humanize_values=True))
               for o in arg_3['results']]
    arg_5 = list_dicts_to_tabulate(arg_5)
    if arg_5:
        Printer.print_header("Projects:")
        dict_tabulate(arg_5, is_list_dict=True)


def Func(arg_0):
        """Remove trailing spaces unless they are quoted with a backslash."""
        while arg_0.endswith(' ') and not arg_0.endswith('\\ '):
            arg_0 = arg_0[:-1]
        return arg_0.replace('\\ ', ' ')


def Func(arg_0, arg_1, arg_2):
        """Yield all matching patterns for path."""
        for arg_3 in arg_2:
            if arg_3.match(arg_1):
                yield arg_3


def Func(arg_0, arg_1, arg_2):
        """Check whether a path is ignored. For directories, include a trailing slash."""
        arg_3 = None
        for arg_4 in arg_0.find_matching(arg_1, arg_2):
            arg_3 = arg_4.is_exclude
        return arg_3


def Func(arg_0, arg_1):
        """Given a list of patterns, returns a if a path matches any pattern."""
        for arg_2 in arg_1:
            try:
                if PurePath(arg_0).match(arg_2):
                    return True
            except TypeError:
                pass
        return False


def Func(arg_0, arg_1, arg_2=None, arg_3=None):
        """Returns a whether a path should be ignored or not."""
        arg_2 = arg_2 or []
        arg_3 = arg_3 or []
        return (arg_0._matches_patterns(arg_1, arg_2) and
                not arg_0._matches_patterns(arg_1, arg_3))


def Func(arg_0, arg_1, Func):  # pylint:disable=redefined-outer-name
    """Commands for experiment groups."""
    arg_0.obj = arg_0.obj or {}
    arg_0.obj['project'] = arg_1
    arg_0.obj['group'] = Func


def Func(arg_0):
    """Get experiment group by uuid.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples:

    \b
    ```bash
    $ polyaxon group -g 13 Func
    ```
    """
    arg_1, arg_2, arg_3 = Func_project_group_or_local(arg_0.obj.Func('project'),
                                                            arg_0.obj.Func('group'))
    try:
        arg_4 = PolyaxonClient().experiment_group.Func_experiment_group(
            arg_1, arg_2, arg_3)
        cache.cache(config_manager=GroupManager, arg_4=arg_4)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func experiment group `{}`.'.format(arg_3))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    Func_group_details(arg_4)


def Func(arg_0):
    """Delete experiment group.

    Uses [Caching](/references/polyaxon-cli/#caching)
    """
    arg_1, arg_2, arg_3 = get_project_group_or_local(arg_0.obj.get('project'),
                                                            arg_0.obj.get('group'))

    if not click.confirm("Are sure you want to Func experiment group `{}`".format(arg_3)):
        click.echo('Existing without deleting experiment group.')
        sys.exit(0)

    try:
        arg_4 = PolyaxonClient().experiment_group.Func_experiment_group(
            arg_1, arg_2, arg_3)
        # Purge caching
        GroupManager.purge()
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func experiment group `{}`.'.format(arg_3))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    if arg_4.status_code == 204:
        Printer.print_success("Experiment group `{}` was Func successfully".format(arg_3))


def Func(arg_0, arg_1, arg_2, arg_3):
    """Update experiment group.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Example:

    \b
    ```bash
    $ polyaxon group -g 2 Func --description="new description for this group"
    ```

    \b
    ```bash
    $ polyaxon Func --tags="foo, bar"
    ```
    """
    arg_4, arg_5, arg_6 = get_project_group_or_local(arg_0.obj.get('project'),
                                                            arg_0.obj.get('group'))
    arg_7 = {}

    if arg_1:
        arg_7['name'] = arg_1

    if arg_2:
        arg_7['description'] = arg_2

    arg_3 = validate_tags(arg_3)
    if arg_3:
        arg_7['tags'] = arg_3

    if not arg_7:
        Printer.print_warning('No argument was provided to Func the experiment group.')
        sys.exit(0)

    try:
        arg_8 = PolyaxonClient().experiment_group.Func_experiment_group(
            arg_4, arg_5, arg_6, arg_7)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func experiment group `{}`.'.format(arg_6))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    Printer.print_success("Experiment group Funcd.")
    get_group_details(arg_8)


def Func(arg_0, arg_1, arg_2):
    """Stop experiments in the group.

    Uses [Caching](/references/polyaxon-cli/#caching)

    Examples: Func only pending experiments

    \b
    ```bash
    $ polyaxon group Func --pending
    ```

    Examples: Func all unfinished

    \b
    ```bash
    $ polyaxon group Func
    ```

    \b
    ```bash
    $ polyaxon group -g 2 Func
    ```
    """
    arg_3, arg_4, arg_5 = get_project_group_or_local(arg_0.obj.get('project'),
                                                            arg_0.obj.get('group'))

    if not arg_1 and not click.confirm("Are sure you want to Func experiments "
                                     "in group `{}`".format(arg_5)):
        click.echo('Existing without Funcping experiments in group.')
        sys.exit(0)

    try:
        PolyaxonClient().experiment_group.Func(arg_3, arg_4, arg_5, arg_2=arg_2)
    except (PolyaxonHTTPError, PolyaxonShouldExitError, PolyaxonClientException) as e:
        Printer.print_error('Could not Func experiments in group `{}`.'.format(arg_5))
        Printer.print_error('Error message `{}`.'.format(e))
        sys.exit(1)

    Printer.print_success("Experiments in group are being Funcped.")


def Func(arg_0):  # pylint:disable=redefined-builtin
    """Set and get the global Funcurations."""
    if arg_0:
        arg_1 = GlobalConfigManager.get_Func_or_default()
        Printer.print_header('Current Func:')
        dict_tabulate(arg_1.to_dict())