def Func(arg_0, arg_1):
    """
    Preprocess mnist dataset.
    Normalize and transform into Sample of RDDs.
    """
    arg_2 = get_mnist(arg_0, "train", arg_1.dataPath)\
        .map(lambda rec_tuple: (normalizer(rec_tuple[0], mnist.TRAIN_MEAN, mnist.TRAIN_STD),
                                rec_tuple[1]))\
        .map(lambda t: Sample.from_ndarray(t[0], t[1]))
    arg_3 = get_mnist(arg_0, "test", arg_1.dataPath)\
        .map(lambda rec_tuple: (normalizer(rec_tuple[0], mnist.TEST_MEAN, mnist.TEST_STD),
                                rec_tuple[1]))\
        .map(lambda t: Sample.from_ndarray(t[0], t[1]))
    return arg_2, arg_3


def Func(arg_0):
    """
    When to end the optimization based on input option.
    """
    if arg_0.endTriggerType.lower() == "epoch":
        return MaxEpoch(arg_0.endTriggerNum)
    else:
        return MaxIteration(arg_0.endTriggerNum)


def Func(arg_0, arg_1, arg_2):
    """
    Set validation and checkpoint for distributed optimizer.
    """
    arg_0.set_validation(
        batch_size=arg_2.batchSize,
        val_rdd=arg_1,
        trigger=EveryEpoch(),
        val_method=[Top1Accuracy()]
    )
    arg_0.set_checkpoint(EveryEpoch(), arg_2.checkpointPath)


def Func(arg_0):
        """ Return the broadcasted Func
        """
        if not hasattr(arg_0, "_Func") and arg_0._path is not None:
            arg_0._Func = arg_0._load(arg_0._path)
        return arg_0._Func


def Func(arg_0, *arg_1):
    """ Call Java Function """
    arg_2 = _get_gateway()
    arg_1 = [_py2java(arg_2, a) for a in arg_1]
    arg_3 = arg_0(*arg_1)
    return _java2py(arg_2, arg_3)


def Func(arg_0):
    """ Return a JavaRDD of Object by unpickling

    It will convert each Python object into Java object by Pyrolite, whenever
    the RDD is serialized in batch or not.
    """
    arg_0 = arg_0._reserialize(AutoBatchedSerializer(PickleSerializer()))
    return \
        arg_0.ctx._jvm.org.apache.spark.bigdl.api.python.BigDLSerDe.pythonToJava(
            arg_0._jrdd, True)


def Func(arg_0, arg_1):
    """ Convert Python object into Java """
    if isinstance(arg_1, RDD):
        arg_1 = _to_java_object_rdd(arg_1)
    elif isinstance(arg_1, DataFrame):
        arg_1 = arg_1._jdf
    elif isinstance(arg_1, SparkContext):
        arg_1 = arg_1._jsc
    elif isinstance(arg_1, (list, tuple)):
        arg_1 = ListConverter().convert([Func(arg_0, x) for x in arg_1],
                                      arg_0._gateway_client)
    elif isinstance(arg_1, dict):
        arg_2 = {}
        for (arg_3, arg_4) in arg_1.items():
            arg_2[arg_3] = Func(arg_0, arg_4)
        arg_1 = MapConverter().convert(arg_2, arg_0._gateway_client)
    elif isinstance(arg_1, JavaValue):
        arg_1 = arg_1.value
    elif isinstance(arg_1, JavaObject):
        pass
    elif isinstance(arg_1, (int, long, float, bool, bytes, unicode)):
        pass
    else:
        arg_5 = bytearray(PickleSerializer().dumps(arg_1))
        arg_1 = arg_0.jvm.org.apache.spark.bigdl.api.python.BigDLSerDe.loads(arg_5)
    return arg_1


def Func(arg_0, arg_1=None):
    """ Convert to a bigdl activation layer
        given the name of the activation as a string  """
    import bigdl.nn.layer as BLayer
    arg_2 = None
    arg_0 = arg_0.lower()
    if arg_0 == "tanh":
        arg_2 = BLayer.Tanh()
    elif arg_0 == "sigmoid":
        arg_2 = BLayer.Sigmoid()
    elif arg_0 == "hard_sigmoid":
        arg_2 = BLayer.HardSigmoid()
    elif arg_0 == "relu":
        arg_2 = BLayer.ReLU()
    elif arg_0 == "softmax":
        arg_2 = BLayer.SoftMax()
    elif arg_0 == "softplus":
        arg_2 = BLayer.SoftPlus(beta=1.0)
    elif arg_0 == "softsign":
        arg_2 = BLayer.SoftSign()
    elif arg_0 == "linear":
        arg_2 = BLayer.Identity()
    else:
        raise Exception("Unsupported activation type: %s" % arg_0)
    if not arg_1:
        arg_2.set_name(arg_1)
    return arg_2


def Func(arg_0, arg_1, arg_2="float"):
        """
        Convert a ndarray to a DenseTensor which would be used in Java side.

        >>> import numpy as np
        >>> from bigdl.util.common import JTensor
        >>> from bigdl.util.common import callBigDlFunc
        >>> np.random.seed(123)
        >>> data = np.random.uniform(0, 1, (2, 3)).astype("float32")
        >>> result = JTensor.Func(data)
        >>> expected_storage = np.array([[0.69646919, 0.28613934, 0.22685145], [0.55131477, 0.71946895, 0.42310646]])
        >>> expected_shape = np.array([2, 3])
        >>> np.testing.assert_allclose(result.storage, expected_storage, rtol=1e-6, atol=1e-6)
        >>> np.testing.assert_allclose(result.shape, expected_shape)
        >>> data_back = result.to_ndarray()
        >>> (data == data_back).all()
        True
        >>> tensor1 = callBigDlFunc("float", "testTensor", JTensor.Func(data))  # noqa
        >>> array_from_tensor = tensor1.to_ndarray()
        >>> (array_from_tensor == data).all()
        True
        """
        if arg_1 is None:
            return None
        assert isinstance(arg_1, np.ndarray), \
            "input should be a np.ndarray, not %s" % type(arg_1)
        return arg_0(arg_1,
                   arg_1.shape if arg_1.shape else (arg_1.size),
                   arg_2)


def Func(arg_0):
        """
        get label as ndarray from ImageFeature
        """
        arg_1 = callBigDlFunc(arg_0.bigdl_type, "imageFeatureToLabelTensor", arg_0.value)
        return arg_1.to_ndarray()


def Func(arg_0, arg_1, arg_2, arg_3="float"):
        """
        Read parquet file as DistributedImageFrame
        """
        return DistributedImageFrame(jvalue=callBigDlFunc(arg_3, "readParquet", arg_1, arg_2))


def Func(arg_0, arg_1, arg_2, arg_3, arg_4 = 1, arg_5="float"):
        """
        write ImageFrame as parquet file
        """
        return callBigDlFunc(arg_5, "writeParquet", arg_1, arg_2, arg_3, arg_4)


def Func(arg_0, arg_1="floats", arg_2=True):
        """
        get image from ImageFrame
        """
        return arg_0.image_frame.Func(arg_1, arg_2)


def Func(arg_0, arg_1="floats", arg_2=True):
        """
        get image list from ImageFrame
        """
        arg_3 = callBigDlFunc(arg_0.bigdl_type,
                                   "localImageFrameToImageTensor", arg_0.value, arg_1, arg_2)
        return map(lambda tensor: tensor.to_ndarray(), arg_3)


def Func(arg_0):
        """
        get label rdd from ImageFrame
        """
        arg_1 = callBigDlFunc(arg_0.bigdl_type, "distributedImageFrameToLabelTensorRdd", arg_0.value)
        return arg_1.map(lambda tensor: tensor.to_ndarray())


def Func(arg_0, arg_1="predict"):
        """
        get prediction rdd from ImageFrame
        """
        arg_2 = callBigDlFunc(arg_0.bigdl_type, "distributedImageFrameToPredict", arg_0.value, arg_1)
        return arg_2.map(lambda predict: (predict[0], predict[1].to_ndarray()) if predict[1] else (predict[0], None))


def Func(arg_0, arg_1, arg_2=None, arg_3=None, arg_4=False):
        """Generates output Funcions for the input samples,
        processing the samples in a batched way.

        # Arguments
            x: the input data, as a Numpy array or list of Numpy array for local mode.
               as RDD[Sample] for distributed mode
            is_distributed: used to control run in local or cluster. the default value is False
        # Returns
            A Numpy array or RDD[Sample] of Funcions.
        """
        if arg_2 or arg_3:
            raise Exception("we don't support batch_size or verbose for now")
        if arg_4:
            if isinstance(arg_1, np.ndarray):
                arg_5 = to_sample_rdd(arg_1, np.zeros([arg_1.shape[0]]))
            #  np.asarray(self.bmodel.Func(x_rdd).collect())
            elif isinstance(arg_1, RDD):
                arg_5 = arg_1
            return arg_0.bmodel.Func(arg_5)
        else:
            if isinstance(arg_1, np.ndarray):
                return arg_0.bmodel.Func_local(arg_1)
        raise Exception("not supported type: %s" % arg_1)


def Func(arg_0, arg_1):
        """
        Apply the Funcer to the images in "inputCol" and store the Funced result
        into "outputCols"
        """
        arg_0._transfer_params_to_java()
        return callBigDlFunc(arg_0.bigdl_type, "dlImageTransform", arg_0.value, arg_1)


def Func(arg_0, arg_1):
    """
    Save a Keras model definition to JSON with given path
    """
    arg_2 = arg_0.to_json()
    with open(arg_1, "w") as json_file:
        json_file.write(arg_2)


def Func():
    """
    Define a convnet model in Keras 1.2.2
    """
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Activation, Flatten
    from keras.layers import Convolution2D, MaxPooling2D

    arg_0 = Sequential()
    arg_0.add(Convolution2D(32, 3, 3, border_mode='valid',
                                  input_shape=input_shape))
    arg_0.add(Activation('relu'))
    arg_0.add(Convolution2D(32, 3, 3))
    arg_0.add(Activation('relu'))
    arg_0.add(MaxPooling2D(pool_size=(2, 2)))
    arg_0.add(Dropout(0.25))
    arg_0.add(Flatten())
    arg_0.add(Dense(128))
    arg_0.add(Activation('relu'))
    arg_0.add(Dropout(0.5))
    arg_0.add(Dense(10))
    arg_0.add(Activation('softmax'))
    return arg_0


def Func(arg_0, arg_1):
        """
        Set weights for this layer

        :param weights: a list of numpy arrays which represent weight and bias
        :return:

        >>> linear = Linear(3,2)
        creating: createLinear
        >>> linear.Func([np.array([[1,2,3],[4,5,6]]), np.array([7,8])])
        >>> weights = linear.get_weights()
        >>> weights[0].shape == (2,3)
        True
        >>> np.testing.assert_allclose(weights[0][0], np.array([1., 2., 3.]))
        >>> np.testing.assert_allclose(weights[1], np.array([7., 8.]))
        >>> relu = ReLU()
        creating: createReLU
        >>> from py4j.protocol import Py4JJavaError
        >>> try:
        ...     relu.Func([np.array([[1,2,3],[4,5,6]]), np.array([7,8])])
        ... except Py4JJavaError as err:
        ...     print(err.java_exception)
        ...
        java.lang.IllegalArgumentException: requirement failed: this layer does not have weight/bias
        >>> relu.get_weights()
        The layer does not have weight/bias
        >>> add = Add(2)
        creating: createAdd
        >>> try:
        ...     add.Func([np.array([7,8]), np.array([1,2])])
        ... except Py4JJavaError as err:
        ...     print(err.java_exception)
        ...
        java.lang.IllegalArgumentException: requirement failed: the number of input weight/bias is not consistant with number of weight/bias of this layer, number of input 1, number of output 2
        >>> cAdd = CAdd([4, 1])
        creating: createCAdd
        >>> cAdd.Func(np.ones([4, 1]))
        >>> (cAdd.get_weights()[0] == np.ones([4, 1])).all()
        True
        """
        arg_2 = [JTensor.from_ndarray(param, arg_0.bigdl_type) for param in to_list(arg_1)]
        callBigDlFunc(arg_0.bigdl_type, "setWeights", arg_0.value, arg_2)


def Func(arg_0, arg_1="float"):
        """
        Load a pre-trained Torch model.

        :param path: The path containing the pre-trained model.
        :return: A pre-trained model.
        """
        arg_2 = callBigDlFunc(arg_1, "loadTorch", arg_0)
        return Layer.of(arg_2)


def Func(arg_0=None, arg_1=None, arg_2=False):
        """
        Load a pre-trained Keras model.

        :param json_path: The json path containing the keras model definition.
        :param hdf5_path: The HDF5 path containing the pre-trained keras model weights with or without the model architecture.
        :return: A bigdl model.
        """
        import arg_3
        try:
            import tensorflow
        except ImportError:
            arg_3.environ['KERAS_BACKEND'] = "theano"
            try:
                # Make theano backend compatible with Python3
                from theano import ifelse
            except ImportError:
                raise Exception("No backend is found for Keras. "
                                "Please install either tensorflow or theano.")
        from bigdl.keras.converter import DefinitionLoader, WeightLoader
        if arg_0 and not arg_1:
            return DefinitionLoader.from_json_path(arg_0)
        elif arg_0 and arg_1:
            return WeightLoader.load_weights_from_json_hdf5(arg_0, arg_1, arg_2=arg_2)
        elif arg_1 and not arg_0:
            arg_5, arg_6 = DefinitionLoader.from_hdf5_path(arg_1)
            WeightLoader.load_weights_from_kmodel(arg_6, arg_5)
            return arg_6


def Func(arg_0, arg_1, arg_2="float"):
        """
        Create a python Criterion by a java criterion object

        :param jcriterion: A java criterion object which created by Py4j
        :return: a criterion.
        """
        arg_3 = Criterion(arg_2, arg_1)
        arg_3.value = arg_1
        arg_3.bigdl_type = arg_2
        return arg_3


def Func(arg_0, arg_1, arg_2=False):
        """
        The file path can be stored in a local file system, HDFS, S3,
        or any Hadoop-supported file system.
        """
        arg_3 = DefinitionLoader.from_json_path(arg_0)
        arg_4 = BCommon.text_from_path(arg_0)
        arg_5 = model_from_json(arg_4)
        WeightLoader.load_weights_from_hdf5(arg_3, arg_5, arg_1, arg_2)
        return arg_3


def Func():
    """
    Load IMDB dataset
    Transform input data into an RDD of Sample
    """
    from keras.preprocessing import sequence
    from keras.datasets import imdb
    (arg_0, arg_1), (arg_2, arg_3) = imdb.load_data(nb_words=20000)
    arg_0 = sequence.pad_sequences(arg_0, maxlen=100)
    arg_2 = sequence.pad_sequences(arg_2, maxlen=100)
    return arg_0, arg_1, arg_2, arg_3


def Func():
    """
    Define a recurrent convolutional model in Keras 1.2.2
    """
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Activation
    from keras.layers import Embedding
    from keras.layers import LSTM
    from keras.layers import Convolution1D, MaxPooling1D
    arg_0 = Sequential()
    arg_0.add(Embedding(20000, 128, input_length=100))
    arg_0.add(Dropout(0.25))
    arg_0.add(Convolution1D(nb_filter=64,
                                  filter_length=5,
                                  border_mode='valid',
                                  activation='relu',
                                  subsample_length=1))
    arg_0.add(MaxPooling1D(pool_length=4))
    arg_0.add(LSTM(70))
    arg_0.add(Dense(1))
    arg_0.add(Activation('sigmoid'))
    return arg_0


def Func(arg_0):
        """
        Return a list of shape tuples if there are multiple inputs.
        Return one shape tuple otherwise.
        """
        arg_1 = callBigDlFunc(arg_0.bigdl_type, "getInputShape",
                              arg_0.value)
        return arg_0.__process_shape(arg_1)


def Func(arg_0):
        """
        Return a list of shape tuples if there are multiple outputs.
        Return one shape tuple otherwise.
        """
        arg_1 = callBigDlFunc(arg_0.bigdl_type, "getOutputShape",
                               arg_0.value)
        return arg_0.__process_shape(arg_1)


def Func(arg_0="train", arg_1="/tmp/mnist"):
    """
    Get mnist dataset with features and label as ndarray.
    Data would be downloaded automatically if it doesn't present at the specific location.

    :param data_type: "train" for training data and "test" for testing data.
    :param location: Location to store mnist dataset.
    :return: (features: ndarray, label: ndarray)
    """
    arg_2, arg_3 = mnist.read_data_sets(arg_1, arg_0)
    return arg_2, arg_3 + 1


def Func(arg_0):
    """
    Parse or download movielens 1m  data if train_dir is empty.

    :param data_dir: The directory storing the movielens data
    :return: a 2D numpy array with user index and item index in each row
    """
    arg_1 = 'ml-1m.zip'
    arg_2 = base.maybe_download(arg_1, arg_0, SOURCE_URL + arg_1)
    arg_3 = zipfile.ZipFile(arg_2, 'r')
    arg_4 = os.path.join(arg_0, "ml-1m")
    if not os.path.exists(arg_4):
        print("Extracting %s to %s" % (arg_2, arg_0))
        arg_3.extractall(arg_0)
        arg_3.close()
    arg_5 = os.path.join(arg_4,"ratings.dat")

    arg_6 = [i.strip().split("::") for i in open(arg_5,"r").readlines()]
    arg_7 = np.array(arg_6).astype(int)
    return arg_7


def Func():
    """
    Get and return the jar path for bigdl if exists.
    """
    if os.getenv("BIGDL_CLASSPATH"):
        return os.environ["BIGDL_CLASSPATH"]
    arg_0 = os.path.abspath(__file__ + "/../../")
    arg_1 = glob.glob(os.path.join(arg_0, "share/lib/*.jar"))
    if arg_1:
        assert len(arg_1) == 1, "Expecting one jar: %s" % len(arg_1)
        return arg_1[0]
    return ""


def Func(arg_0):
    """
    Export variable tensors from the checkpoint files.

    :param checkpoint_path: tensorflow checkpoint path
    :return: dictionary of tensor. The key is the variable name and the value is the numpy
    """
    arg_1 = tf.train.NewCheckpointReader(arg_0)

    # Get tensor name list
    arg_2 = filter(lambda n: n!='global_step',
                          arg_1.get_variable_to_shape_map().keys())
    # Prepare key-value dictionary
    arg_3 = {}
    for arg_4 in arg_2:
        arg_3[arg_4] = arg_1.get_tensor(arg_4)

    return arg_3


def Func(arg_0, arg_1, arg_2="float"):
    """
    Save a variable dictionary to a Java object file, so it can be read by BigDL

    :param tensors: tensor dictionary
    :param target_path: where is the Java object file store
    :param bigdl_type: model variable numeric type
    :return: nothing
    """
    import numpy as np
    arg_3 = {}
    for arg_4 in arg_0.keys():
        if not isinstance(arg_0[arg_4], np.ndarray):
            arg_5 = np.array(arg_0[arg_4])
        else:
            arg_5 = arg_0[arg_4]
        arg_3[arg_4] = JTensor.from_ndarray(arg_5)

    callBigDlFunc(arg_2, "saveTensorDictionary", arg_3, arg_1)


def Func(arg_0, arg_1):
    """
    Expand and tile tensor along given axis

    Args:
        units: tf tensor with dimensions [batch_size, time_steps, n_input_features]
        axis: axis along which expand and tile. Must be 1 or 2

    """
    assert arg_1 in (1, 2)
    arg_2 = K.int_shape(arg_0)[1]
    arg_3 = [1, 1, 1, 1]
    arg_3[arg_1] = arg_2
    if arg_1 == 1:
        arg_4 = Reshape(target_shape=( (1,) + K.int_shape(arg_0)[1:] ))(arg_0)
    else:
        arg_4 = Reshape(target_shape=(K.int_shape(arg_0)[1:2] + (1,) + K.int_shape(arg_0)[2:]))(arg_0)
    return K.tile(arg_4, arg_3)


def Func(arg_0, arg_1, arg_2, arg_3=1.0, arg_4="Func"):
    """Simple attention without any conditions.

       Computes weighted sum of memory elements.
    """
    with tf.variable_scope(arg_4):
        arg_5, arg_6, arg_7 = tf.unstack(tf.shape(arg_0))
        arg_8 = tf.nn.dropout(arg_0, arg_3=arg_3, noise_shape=[arg_5, 1, arg_7])
        arg_9 = tf.layers.dense(tf.layers.dense(arg_8, arg_1, activation=tf.nn.tanh), 1, use_bias=False)
        arg_9 = softmax_mask(tf.squeeze(arg_9, [2]), arg_2)
        arg_10 = tf.expand_dims(tf.nn.softmax(arg_9), axis=2)
        arg_11 = tf.reduce_sum(arg_10 * arg_0, axis=1)
        return arg_11


def Func(arg_0, arg_1, arg_2, arg_3, arg_4="Func"):
    """Computes weighted sum of inputs conditioned on state"""
    with tf.variable_scope(arg_4):
        arg_5 = tf.concat([tf.tile(tf.expand_dims(arg_1, axis=1), [1, tf.shape(arg_0)[1], 1]), arg_0], axis=2)
        arg_6 = tf.layers.dense(tf.layers.dense(arg_5, arg_2, activation=tf.nn.tanh), 1, use_bias=False)
        arg_6 = softmax_mask(tf.squeeze(arg_6, [2]), arg_3)
        arg_7 = tf.expand_dims(tf.nn.softmax(arg_6), axis=2)
        arg_8 = tf.reduce_sum(arg_7 * arg_0, axis=1)
        return arg_8, arg_6


def Func(arg_0, arg_1, arg_2=4,
                 arg_3=False):
  """Computes BLEU score of translated segments against one or more references.

  Args:
    reference_corpus: list of lists of references for each translation. Each
        reference should be tokenized into a list of tokens.
    translation_corpus: list of translations to score. Each translation
        should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.

  Returns:
    3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram
    precisions and brevity penalty.
  """
  arg_4 = [0] * arg_2
  arg_5 = [0] * arg_2
  arg_6 = 0
  arg_7 = 0
  for (arg_8, arg_9) in zip(arg_0,
                                       arg_1):
    arg_6 += min(len(arg_10) for arg_10 in arg_8)
    arg_7 += len(arg_9)

    arg_11 = collections.Counter()
    for arg_12 in arg_8:
      arg_11 |= _get_ngrams(arg_12, arg_2)
    arg_13 = _get_ngrams(arg_9, arg_2)
    arg_14 = arg_13 & arg_11
    for arg_15 in arg_14:
      arg_4[len(arg_15)-1] += arg_14[arg_15]
    for arg_16 in range(1, arg_2+1):
      arg_17 = len(arg_9) - arg_16 + 1
      if arg_17 > 0:
        arg_5[arg_16-1] += arg_17

  arg_18 = [0] * arg_2
  for arg_19 in range(0, arg_2):
    if arg_3:
      arg_18[arg_19] = ((arg_4[arg_19] + 1.) /
                       (arg_5[arg_19] + 1.))
    else:
      if arg_5[arg_19] > 0:
        arg_18[arg_19] = (float(arg_4[arg_19]) /
                         arg_5[arg_19])
      else:
        arg_18[arg_19] = 0.0

  if min(arg_18) > 0:
    arg_20 = sum((1. / arg_2) * math.log(p) for p in arg_18)
    arg_21 = math.exp(arg_20)
  else:
    arg_21 = 0

  arg_22 = float(arg_7) / arg_6

  if arg_22 > 1.0:
    arg_23 = 1.
  else:
    arg_23 = math.exp(1 - 1. / arg_22)

  arg_24 = arg_21 * arg_23

  return (arg_24, arg_18, arg_23, arg_22, arg_7, arg_6)


def Func(arg_0, arg_1, arg_2):
    """
    Dump the trained weights from a model to a HDF5 file.
    """

    def _get_outname(arg_3):
        arg_4 = re.sub(':0$', '', arg_3)
        arg_4 = arg_4.lstrip('lm/')
        arg_4 = re.sub('/rnn/', '/RNN/', arg_4)
        arg_4 = re.sub('/multi_rnn_cell/', '/MultiRNNCell/', arg_4)
        arg_4 = re.sub('/cell_', '/Cell', arg_4)
        arg_4 = re.sub('/lstm_cell/', '/LSTMCell/', arg_4)
        if '/RNN/' in arg_4:
            if 'projection' in arg_4:
                arg_4 = re.sub('projection/kernel', 'W_P_0', arg_4)
            else:
                arg_4 = re.sub('/kernel', '/W_0', arg_4)
                arg_4 = re.sub('/bias', '/B', arg_4)
        return arg_4

    arg_5 = tf.train.latest_checkpoint(arg_0)

    arg_6 = tf.ConfigProto(allow_soft_placement=True)
    with tf.Graph().as_default():
        with tf.Session(arg_6=arg_6) as sess:
            with tf.variable_scope('lm'):
                LanguageModel(arg_2, False)  # Create graph
                # we use the "Saver" class to load the variables
                arg_7 = tf.train.Saver()
                arg_7.restore(sess, arg_5)

            with h5py.File(arg_1, 'w') as fout:
                for arg_8 in tf.trainable_variables():
                    if arg_8.name.find('softmax') >= 0:
                        # don't dump these
                        continue
                    arg_4 = _get_outname(arg_8.name)
                    # print("Saving variable {0} with name {1}".format(
                    #     v.name, outname))
                    arg_9 = arg_8.get_shape().as_list()
                    arg_10 = fout.create_dataset(arg_4, arg_9, dtype='float32')
                    arg_11 = sess.run([arg_8])[0]
                    arg_10[...] = arg_11


def Func(arg_0: [arg_1[arg_2], arg_1[arg_1[arg_2]], arg_3.ndarray], arg_5:  [arg_6, arg_3.ndarray]) -> arg_3.ndarray:
    """
    Convert labels to one-hot vectors for multi-class multi-label classification

    Args:
        labels: list of samples where each sample is a class or a list of classes which sample belongs with
        classes: array of classes' names

    Returns:
        2d array with one-hot representation of given samples
    """
    arg_7 = len(arg_5)
    arg_8 = []
    for arg_9 in arg_0:
        arg_10 = arg_3.zeros(arg_7)
        if isinstance(arg_9, arg_6):
            for arg_11 in arg_9:
                if arg_11 not in arg_5:
                    log.warning('Unknown intent {} detected. Assigning no class'.format(arg_11))
                else:
                    arg_10[arg_3.where(arg_3.array(arg_5) == arg_11)[0]] = 1
        else:
            arg_10[arg_3.where(arg_3.array(arg_5) == arg_9)[0]] = 1
        arg_8.append(arg_10)
    arg_8 = arg_3.asarray(arg_8)
    return arg_8


def Func(arg_0) -> None:
        """Checks existence of the model file, Funcs the model if the file exists"""

        # Checks presence of the model files
        if arg_0.Func_path.exists():
            arg_1 = str(arg_0.Func_path.resolve())
            log.info('[Funcing model from {}]'.format(arg_1))
            arg_0._net.Func(arg_1)


def Func(arg_0):
        """
        Extract values of momentum variables from optimizer

        Returns:
            optimizer's `rho` or `beta_1`
        """
        arg_1 = arg_0.get_optimizer()
        if hasattr(arg_1, 'rho'):
            return arg_1.rho
        elif hasattr(arg_1, 'beta_1'):
            return arg_1.beta_1
        return None


def Func(arg_0, arg_1: arg_2 = None, arg_3: arg_2 = None):
        """
        Update graph variables setting giving `learning_rate` and `momentum`

        Args:
            learning_rate: learning rate value to be set in graph (set if not None)
            momentum: momentum value to be set in graph (set if not None)

        Returns:
            None
        """
        if arg_1 is not None:
            K.set_value(arg_0.get_learning_rate_variable(), arg_1)
            # log.info(f"Learning rate = {learning_rate}")
        if arg_3 is not None:
            K.set_value(arg_0.get_momentum_variable(), arg_3)


def Func(arg_0: arg_1, arg_2: arg_3 = False,
                 arg_4: arg_5[arg_1] = None) -> Tuple[arg_1]:
    """Converts word to a tuple of symbols, optionally converts it to lowercase
    and adds capitalization label.

    Args:
        word: input word
        to_lower: whether to lowercase
        append_case: whether to add case mark
            ('<FIRST_UPPER>' for first capital and '<ALL_UPPER>' for all caps)

    Returns:
        a preprocessed word
    """
    if all(arg_6.isupper() for arg_6 in arg_0) and len(arg_0) > 1:
        arg_7 = "<ALL_UPPER>"
    elif arg_0[0].isupper():
        arg_7 = "<FIRST_UPPER>"
    else:
        arg_7 = None
    if arg_2:
        arg_0 = arg_0.lower()
    if arg_0.isdigit():
        arg_8 = ["<DIGIT>"]
    elif arg_0.startswith("http://") or arg_0.startswith("www."):
        arg_8 = ["<HTTP>"]
    else:
        arg_8 = list(arg_0)
    if arg_2 and arg_7 is not None:
        if arg_4 == "first":
            arg_8 = [arg_7] + arg_8
        elif arg_4 == "last":
            arg_8 = arg_8 + [arg_7]
    return tuple(arg_8)


def Func(arg_0: arg_1.Tensor,
                arg_3: arg_4,
                arg_5=3,
                arg_6=False,
                arg_7=False,
                arg_8=None,
                arg_9=False):
    """ Number of convolutional layers stacked on top of each other

    Args:
        units: a tensorflow tensor with dimensionality [None, n_tokens, n_features]
        n_hidden_list: list with number of hidden units at the ouput of each layer
        filter_width: width of the kernel in tokens
        use_batch_norm: whether to use batch normalization between layers
        use_dilation: use power of 2 dilation scheme [1, 2, 4, 8 .. ] for layers 1, 2, 3, 4 ...
        training_ph: boolean placeholder determining whether is training phase now or not.
            It is used only for batch normalization to determine whether to use
            current batch average (std) or memory stored average (std)
        add_l2_losses: whether to add l2 losses on network kernels to
                tf.GraphKeys.REGULARIZATION_LOSSES or not

    Returns:
        units: tensor at the output of the last convolutional layer
    """
    arg_10 = arg_1.nn.l2_loss if arg_9 else None
    for arg_11, arg_12 in enumerate(arg_3):
        if arg_7:
            arg_13 = 2 ** arg_11
        else:
            arg_13 = 1
        arg_0 = arg_1.layers.conv1d(arg_0,
                                 arg_12,
                                 arg_5,
                                 padding='same',
                                 arg_13=arg_13,
                                 kernel_initializer=INITIALIZER(),
                                 kernel_regularizer=arg_10)
        if arg_6:
            assert arg_8 is not None
            arg_0 = arg_1.layers.batch_normalization(arg_0, training=arg_8)
        arg_0 = arg_1.nn.relu(arg_0)
    return arg_0


def Func(arg_0: arg_1.Tensor,
                        arg_3: arg_4,
                        arg_5=3,
                        arg_6=False,
                        arg_7=False,
                        arg_8=None):
    """ Highway convolutional network. Skip connection with gating
        mechanism.

    Args:
        units: a tensorflow tensor with dimensionality [None, n_tokens, n_features]
        n_hidden_list: list with number of hidden units at the output of each layer
        filter_width: width of the kernel in tokens
        use_batch_norm: whether to use batch normalization between layers
        use_dilation: use power of 2 dilation scheme [1, 2, 4, 8 .. ] for layers 1, 2, 3, 4 ...
        training_ph: boolean placeholder determining whether is training phase now or not.
            It is used only for batch normalization to determine whether to use
            current batch average (std) or memory stored average (std)
    Returns:
        units: tensor at the output of the last convolutional layer
                with dimensionality [None, n_tokens, n_hidden_list[-1]]
    """

    for arg_9, arg_10 in enumerate(arg_3):
        arg_11 = arg_0
        # Projection if needed
        if arg_11.get_shape().as_list()[-1] != arg_10:
            arg_11 = arg_1.layers.dense(arg_11, arg_10)
        if arg_7:
            arg_12 = 2 ** arg_9
        else:
            arg_12 = 1
        arg_0 = arg_1.layers.conv1d(arg_0,
                                 arg_10,
                                 arg_5,
                                 padding='same',
                                 arg_12=arg_12,
                                 kernel_initializer=INITIALIZER())
        if arg_6:
            arg_0 = arg_1.layers.batch_normalization(arg_0, training=arg_8)
        arg_13 = arg_1.layers.dense(arg_11, 1, activation=arg_1.sigmoid, kernel_initializer=INITIALIZER())
        arg_11 = arg_13 * arg_11 + (1 - arg_13) * arg_0
        arg_11 = arg_1.nn.relu(arg_11)
    arg_0 = arg_11
    return arg_0


def Func(arg_0=None,
                    arg_1=None,
                    arg_2=None,
                    arg_3=None,
                    arg_4: arg_5 = None,
                    arg_6=True):
    """ Token embedding layer. Create matrix of for token embeddings.
        Can be initialized with given matrix (for example pre-trained
        with word2ve algorithm

    Args:
        token_indices: token indices tensor of type tf.int32
        token_embedding_matrix: matrix of embeddings with dimensionality
            [n_tokens, embeddings_dimension]
        n_tokens: total number of unique tokens
        token_embedding_dim: dimensionality of embeddings, typical 100..300
        name: embedding matrix name (variable name)
        trainable: whether to set the matrix trainable or not

    Returns:
        embedded_tokens: tf tensor of size [B, T, E], where B - batch size
            T - number of tokens, E - token_embedding_dim
    """
    if arg_1 is not None:
        arg_7 = arg_1
        if arg_6:
            Warning('Matrix of embeddings is passed to the Func, '
                    'possibly there is a pre-trained embedding matrix. '
                    'Embeddings paramenters are set to Trainable!')
    else:
        arg_7 = np.random.randn(arg_2, arg_3).astype(np.float32) / np.sqrt(arg_3)
    arg_8 = tf.Variable(arg_7, arg_4=arg_4, arg_6=arg_6)
    arg_9 = tf.nn.embedding_lookup(arg_8, arg_0)
    return arg_9


def Func(arg_0, arg_1, arg_2=1, arg_3=False,
              arg_4=None, arg_5=None, arg_6='Func', arg_7=False):
    """ Fast CuDNN GRU implementation

    Args:
        units: tf.Tensor with dimensions [B x T x F], where
            B - batch size
            T - number of tokens
            F - features

        n_hidden: dimensionality of hidden state
        trainable_initial_states: whether to create a special trainable variable
            to initialize the hidden states of the network or use just zeros
        seq_lengths: tensor of sequence lengths with dimension [B]
        n_layers: number of layers
        input_initial_h: initial hidden state, tensor
        name: name of the variable scope to use
        reuse:whether to reuse already initialized variable

    Returns:
        h - all hidden states along T dimension,
            tf.Tensor with dimensionality [B x T x F]
        h_last - last hidden state, tf.Tensor with dimensionality [B x H]
    """
    with tf.variable_scope(arg_6, arg_7=arg_7):
        arg_8 = tf.contrib.cudnn_rnn.CudnnGRU(num_layers=arg_2,
                                            num_units=arg_1)

        if arg_3:
            arg_9 = tf.get_variable('init_h', [arg_2, 1, arg_1])
            arg_9 = tf.tile(arg_9, (1, tf.shape(arg_0)[0], 1))
        else:
            arg_9 = tf.zeros([arg_2, tf.shape(arg_0)[0], arg_1])

        arg_10 = arg_5 or arg_9

        arg_11, arg_12 = arg_8(tf.transpose(arg_0, (1, 0, 2)), (arg_10, ))
        arg_11 = tf.transpose(arg_11, (1, 0, 2))
        arg_12 = tf.squeeze(arg_12, axis=0)[-1]  # extract last layer state

        # Extract last states if they are provided
        if arg_4 is not None:
            arg_13 = tf.stack([tf.range(tf.shape(arg_11)[0]), arg_4-1], axis=1)
            arg_12 = tf.gather_nd(arg_11, arg_13)

        return arg_11, arg_12


def Func(arg_0, arg_1, arg_2=1, arg_3=False,
                         arg_4=None, arg_5=None, arg_6='cudnn_gru', arg_7=False):
    """ CuDNN Compatible GRU implementation.
        It should be used to load models saved with CudnnGRUCell to run on CPU.

        Args:
            units: tf.Tensor with dimensions [B x T x F], where
                B - batch size
                T - number of tokens
                F - features

            n_hidden: dimensionality of hidden state
            trainable_initial_states: whether to create a special trainable variable
                to initialize the hidden states of the network or use just zeros
            seq_lengths: tensor of sequence lengths with dimension [B]
            n_layers: number of layers
            input_initial_h: initial hidden state, tensor
            name: name of the variable scope to use
            reuse:whether to reuse already initialized variable

        Returns:
            h - all hidden states along T dimension,
                tf.Tensor with dimensionality [B x T x F]
            h_last - last hidden state, tf.Tensor with dimensionality [B x H]
        """
    with tf.variable_scope(arg_6, arg_7=arg_7):

        if arg_3:
            arg_8 = tf.get_variable('init_h', [arg_2, 1, arg_1])
            arg_8 = tf.tile(arg_8, (1, tf.shape(arg_0)[0], 1))
        else:
            arg_8 = tf.zeros([arg_2, tf.shape(arg_0)[0], arg_1])

        arg_9 = arg_5 or arg_8

        with tf.variable_scope('cudnn_gru', arg_7=arg_7):
            def single_cell(): return tf.contrib.cudnn_rnn.CudnnCompatibleGRUCell(arg_1)
            arg_10 = tf.nn.rnn_cell.MultiRNNCell([single_cell() for _ in range(arg_2)])

            arg_0 = tf.transpose(arg_0, (1, 0, 2))

            arg_11, arg_12 = tf.nn.dynamic_rnn(arg_10=arg_10, inputs=arg_0, time_major=True,
                                          initial_state=tuple(tf.unstack(arg_9, axis=0)))
            arg_11 = tf.transpose(arg_11, (1, 0, 2))

            arg_12 = arg_12[-1]  # h_last is tuple: n_layers x batch_size x n_hidden

            # Extract last states if they are provided
            if arg_4 is not None:
                arg_13 = tf.stack([tf.range(tf.shape(arg_11)[0]), arg_4-1], axis=1)
                arg_12 = tf.gather_nd(arg_11, arg_13)

            return arg_11, arg_12


def Func(arg_0, arg_1, arg_2=1, arg_3=None, arg_4=None, arg_5=None,
                          arg_6=None, arg_7='cudnn_lstm', arg_8=False):
    """ CuDNN Compatible LSTM implementation.
        It should be used to load models saved with CudnnLSTMCell to run on CPU.

        Args:
            units: tf.Tensor with dimensions [B x T x F], where
                B - batch size
                T - number of tokens
                F - features
            n_hidden: dimensionality of hidden state
            n_layers: number of layers
            trainable_initial_states: whether to create a special trainable variable
                to initialize the hidden states of the network or use just zeros
            seq_lengths: tensor of sequence lengths with dimension [B]
            initial_h: optional initial hidden state, masks trainable_initial_states
                if provided
            initial_c: optional initial cell state, masks trainable_initial_states
                if provided
            name: name of the variable scope to use
            reuse:whether to reuse already initialized variable

        Returns:
            h - all hidden states along T dimension,
                tf.Tensor with dimensionality [B x T x F]
            h_last - last hidden state, tf.Tensor with dimensionality [B x H]
                where H - number of hidden units
            c_last - last cell state, tf.Tensor with dimensionality [B x H]
                where H - number of hidden units
        """

    with tf.variable_scope(arg_7, arg_8=arg_8):
        if arg_3:
            arg_9 = tf.get_variable('init_h', [arg_2, 1, arg_1])
            arg_9 = tf.tile(arg_9, (1, tf.shape(arg_0)[0], 1))
            arg_10 = tf.get_variable('init_c', [arg_2, 1, arg_1])
            arg_10 = tf.tile(arg_10, (1, tf.shape(arg_0)[0], 1))
        else:
            arg_9 = arg_10 = tf.zeros([arg_2, tf.shape(arg_0)[0], arg_1])

        arg_5 = arg_5 or arg_9
        arg_6 = arg_6 or arg_10

        with tf.variable_scope('cudnn_lstm', arg_8=arg_8):
            def single_cell(): return tf.contrib.cudnn_rnn.CudnnCompatibleLSTMCell(arg_1)

            arg_11 = tf.nn.rnn_cell.MultiRNNCell([single_cell() for _ in range(arg_2)])

            arg_0 = tf.transpose(arg_0, (1, 0, 2))

            arg_12 = tuple([tf.nn.rnn_cell.LSTMStateTuple(ic, ih) for ih, ic in
                          zip(tf.unstack(arg_5, axis=0), tf.unstack(arg_6, axis=0))])

            arg_13, arg_14 = tf.nn.dynamic_rnn(arg_11=arg_11, inputs=arg_0, time_major=True, initial_state=arg_12)

            arg_13 = tf.transpose(arg_13, (1, 0, 2))
            arg_15 = arg_14[-1].h
            arg_16 = arg_14[-1].c

            # Extract last states if they are provided
            if arg_4 is not None:
                arg_17 = tf.stack([tf.range(tf.shape(arg_13)[0]), arg_4-1], axis=1)
                arg_15 = tf.gather_nd(arg_13, arg_17)

            return arg_13, (arg_15, arg_16)


def Func(arg_0,
                 arg_1,
                 arg_2=None,
                 arg_3=1,
                 arg_4=False,
                 arg_5='Func',
                 arg_6=False):
    """ Fast CuDNN Bi-GRU implementation

    Args:
        units: tf.Tensor with dimensions [B x T x F], where
            B - batch size
            T - number of tokens
            F - features
        n_hidden: dimensionality of hidden state
        seq_lengths: number of tokens in each sample in the batch
        n_layers: number of layers
        trainable_initial_states: whether to create a special trainable variable
                to initialize the hidden states of the network or use just zeros
        name: name of the variable scope to use
        reuse:whether to reuse already initialized variable

    Returns:
        h - all hidden states along T dimension,
            tf.Tensor with dimensionality [B x T x F]
        h_last - last hidden state, tf.Tensor with dimensionality [B x H * 2]
            where H - number of hidden units
    """

    with tf.variable_scope(arg_5, arg_6=arg_6):
        if arg_2 is None:
            arg_2 = tf.ones([tf.shape(arg_0)[0]], dtype=tf.int32) * tf.shape(arg_0)[1]
        with tf.variable_scope('Forward'):
            arg_7, arg_8 = cudnn_gru_wrapper(arg_0,
                                                arg_1,
                                                arg_3=arg_3,
                                                arg_4=arg_4,
                                                arg_2=arg_2,
                                                arg_6=arg_6)

        with tf.variable_scope('Backward'):
            arg_9 = tf.reverse_sequence(arg_0, arg_2=arg_2, seq_dim=1, batch_dim=0)
            arg_10, arg_11 = cudnn_gru_wrapper(arg_9,
                                                arg_1,
                                                arg_3=arg_3,
                                                arg_4=arg_4,
                                                arg_2=arg_2,
                                                arg_6=arg_6)
            arg_10 = tf.reverse_sequence(arg_10, arg_2=arg_2, seq_dim=1, batch_dim=0)

    return (arg_7, arg_10), (arg_8, arg_11)


def Func(arg_0,
                  arg_1,
                  arg_2=None,
                  arg_3=1,
                  arg_4=False,
                  arg_5='cudnn_bi_gru',
                  arg_6=False):
    """ Fast CuDNN Bi-LSTM implementation

    Args:
        units: tf.Tensor with dimensions [B x T x F], where
            B - batch size
            T - number of tokens
            F - features
        n_hidden: dimensionality of hidden state
        seq_lengths: number of tokens in each sample in the batch
        n_layers: number of layers
        trainable_initial_states: whether to create a special trainable variable
            to initialize the hidden states of the network or use just zeros
        name: name of the variable scope to use
        reuse:whether to reuse already initialized variable

    Returns:
        h - all hidden states along T dimension,
            tf.Tensor with dimensionality [B x T x F]
        h_last - last hidden state, tf.Tensor with dimensionality [B x H * 2]
            where H - number of hidden units
        c_last - last cell state, tf.Tensor with dimensionality [B x H * 2]
            where H - number of hidden units
        """
    with tf.variable_scope(arg_5, arg_6=arg_6):
        if arg_2 is None:
            arg_2 = tf.ones([tf.shape(arg_0)[0]], dtype=tf.int32) * tf.shape(arg_0)[1]
        with tf.variable_scope('Forward'):
            arg_7, (arg_8, arg_9) = cudnn_lstm_wrapper(arg_0,
                                                              arg_1,
                                                              arg_3=arg_3,
                                                              arg_4=arg_4,
                                                              arg_2=arg_2)

        with tf.variable_scope('Backward'):
            arg_10 = tf.reverse_sequence(arg_0, arg_2=arg_2, seq_dim=1, batch_dim=0)
            arg_11, (arg_12, arg_13) = cudnn_lstm_wrapper(arg_10,
                                                              arg_1,
                                                              arg_3=arg_3,
                                                              arg_4=arg_4,
                                                              arg_2=arg_2)

            arg_11 = tf.reverse_sequence(arg_11, arg_2=arg_2, seq_dim=1, batch_dim=0)
        return (arg_7, arg_11), ((arg_8, arg_9), (arg_12, arg_13))


def Func(arg_0,
                         arg_1,
                         arg_2=None,
                         arg_3=2,
                         arg_4=1.0,
                         arg_5=False,
                         arg_6=False,
                         arg_7='Func',
                         arg_8=False):
    """ Fast CuDNN Stacked Bi-GRU implementation

    Args:
        units: tf.Tensor with dimensions [B x T x F], where
            B - batch size
            T - number of tokens
            F - features
        n_hidden: dimensionality of hidden state
        seq_lengths: number of tokens in each sample in the batch
        n_stacks: number of stacked Bi-GRU
        keep_prob: dropout keep_prob between Bi-GRUs (intra-layer dropout)
        concat_stacked_outputs: return last Bi-GRU output or concat outputs from every Bi-GRU,
        trainable_initial_states: whether to create a special trainable variable
                to initialize the hidden states of the network or use just zeros
        name: name of the variable scope to use
        reuse: whether to reuse already initialized variable

    Returns:
        h - all hidden states along T dimension,
            tf.Tensor with dimensionality [B x T x ((n_hidden * 2) * n_stacks)]
    """
    if arg_2 is None:
        arg_2 = tf.ones([tf.shape(arg_0)[0]], dtype=tf.int32) * tf.shape(arg_0)[1]

    arg_9 = [arg_0]

    with tf.variable_scope(arg_7, arg_8=arg_8):
        for arg_10 in range(arg_3):

            if arg_10 == 0:
                arg_11 = arg_9[-1]
            else:
                arg_11 = variational_dropout(arg_9[-1], arg_4=arg_4)

            (arg_12, arg_13), arg_14 = cudnn_bi_gru(arg_11, arg_1, arg_2,
                                           n_layers=1,
                                           arg_6=arg_6,
                                           arg_7='{}_cudnn_bi_gru'.format(arg_10),
                                           arg_8=arg_8)

            arg_9.append(tf.concat([arg_12, arg_13], axis=2))

    if arg_5:
        return tf.concat(arg_9[1:], axis=2)

    return arg_9[-1]


def Func(arg_0, arg_1, arg_2=(1,)):
    """ Dropout with the same drop mask for all fixed_mask_dims

    Args:
        units: a tensor, usually with shapes [B x T x F], where
            B - batch size
            T - tokens dimension
            F - feature dimension
        keep_prob: keep probability
        fixed_mask_dims: in these dimensions the mask will be the same

    Returns:
        dropped units tensor
    """
    arg_3 = tf.shape(arg_0)
    arg_4 = [arg_3[n] for n in range(len(arg_0.shape))]
    for arg_5 in arg_2:
        arg_4[arg_5] = 1
    return tf.nn.dropout(arg_0, arg_1, arg_4)


def Func(arg_0):
        """Builds the network using Keras.
        """
        arg_1 = kl.Input(shape=(None, MAX_WORD_LENGTH+2), dtype="int32")
        arg_2 = [arg_1]
        arg_3 = arg_0._Func_word_cnn(arg_1)
        if len(arg_0.word_vectorizers) > 0:
            arg_4 = [kl.Input(shape=(None, input_dim), dtype="float32")
                                      for input_dim, dense_dim in arg_0.word_vectorizers]
            arg_2.extend(arg_4)
            arg_5 = [kl.Dense(dense_dim)(arg_4[i])
                                          for i, (_, dense_dim) in enumerate(arg_0.word_vectorizers)]
            arg_3 = kl.Concatenate()([arg_3] + arg_5)
        arg_6, arg_7 = arg_0._Func_basic_network(arg_3)
        arg_8 = {"optimizer": ko.nadam(lr=0.002, clipnorm=5.0),
                        "loss": "categorical_crossentropy", "metrics": ["accuracy"]}
        arg_0.model_ = Model(arg_2, arg_6)
        arg_0.model_.compile(**arg_8)
        if arg_0.verbose > 0:
            arg_0.model_.summary(print_fn=log.info)
        return arg_0


def Func(arg_0, arg_1):
        """Builds word-level network
        """
        arg_1 = kl.Lambda(kb.one_hot, arguments={"num_classes": arg_0.symbols_number_},
                           output_shape=lambda x: tuple(x) + (arg_0.symbols_number_,))(arg_1)
        arg_2 = kl.Dense(arg_0.char_embeddings_size, use_bias=False)(arg_1)
        arg_3 = []
        arg_0.char_output_dim_ = 0
        for arg_5, arg_6 in zip(arg_0.char_window_size, arg_0.char_filters):
            arg_7 = arg_2
            arg_8 = (min(arg_0.char_filter_multiple * arg_5, 200)
                                   if arg_6 is None else arg_6)
            for arg_9 in range(arg_0.char_conv_layers - 1):
                arg_7 = kl.Conv2D(arg_8, (1, arg_5),
                                        padding="same", activation="relu",
                                        data_format="channels_last")(arg_7)
                if arg_0.conv_dropout > 0.0:
                    arg_7 = kl.Dropout(arg_0.conv_dropout)(arg_7)
            arg_7 = kl.Conv2D(arg_8, (1, arg_5),
                                    padding="same", activation="relu",
                                    data_format="channels_last")(arg_7)
            arg_3.append(arg_7)
            arg_0.char_output_dim_ += arg_8
        if len(arg_3) > 1:
            arg_10 = kl.Concatenate(axis=-1)(arg_3)
        else:
            arg_10 = arg_3[0]
        arg_11 = kl.Lambda(kb.max, arguments={"axis": -2})(arg_10)
        if arg_0.intermediate_dropout > 0.0:
            arg_11 = kl.Dropout(arg_0.intermediate_dropout)(arg_11)
        for arg_12 in range(arg_0.char_highway_layers - 1):
            arg_11 = Highway(activation="relu")(arg_11)
            if arg_0.highway_dropout > 0.0:
                arg_11 = kl.Dropout(arg_0.highway_dropout)(arg_11)
        arg_13 = Highway(activation="relu")(arg_11)
        return arg_13


def Func(arg_0, arg_1: arg_2[arg_3, arg_4],
                         arg_5: arg_6 = False) -> List[List[str]]:
        """
        Makes predictions on a single batch

        Args:
            data: a batch of word sequences together with additional inputs
            return_indexes: whether to return tag indexes in vocabulary or tags themselves

        Returns:
            a batch of label sequences
        """
        arg_7 = arg_0._transform_batch(arg_1)
        arg_8, arg_9 = len(arg_7[0]), [len(arg_14) for arg_14 in arg_1[0]]
        arg_10 = arg_0.model_.Func(arg_7)
        arg_11 = np.argmax(arg_10, axis=-1)
        arg_12: List[List[str]] = [None] * arg_8
        for arg_13, (arg_14, arg_15) in enumerate(zip(arg_11, arg_9)):
            arg_14 = arg_14[:arg_15]
            arg_12[arg_13] = arg_14 if arg_5 else arg_0.tags.idxs2toks(arg_14)
        return arg_12


def Func(arg_0, arg_1: arg_2, arg_3: arg_4 =None) -> np.ndarray:
        """Transforms a sentence to Numpy array, which will be the network input.

        Args:
            sent: input sentence
            bucket_length: the width of the bucket

        Returns:
            A 3d array, answer[i][j][k] contains the index of k-th letter
            in j-th word of i-th input sentence.
        """
        arg_3 = arg_3 or len(arg_1)
        arg_5 = np.zeros(shape=(arg_3, MAX_WORD_LENGTH+2), dtype=np.int32)
        for arg_6, arg_7 in enumerate(arg_1):
            arg_5[arg_6, 0] = arg_0.tags.tok2idx("BEGIN")
            arg_8 = min(len(arg_7), MAX_WORD_LENGTH)
            for arg_9, arg_10 in enumerate(arg_7[-arg_8:]):
                arg_5[arg_6, arg_9+1] = arg_0.symbols.tok2idx(arg_10)
            arg_5[arg_6, arg_8+1] = arg_0.tags.tok2idx("END")
            arg_5[arg_6, arg_8+2:] = arg_0.tags.tok2idx("PAD")
        return arg_5


def Func(arg_0: arg_1[arg_2], arg_3: arg_1[arg_2],
                  arg_4: arg_5=(1,), arg_6=arg_7.method1,
                  arg_9=False, arg_10=True) -> float:
    """Calculate BLEU score

    Parameters:
        y_true: list of reference tokens
        y_predicted: list of query tokens
        weights: n-gram weights
        smoothing_function: SmoothingFunction
        auto_reweigh: Option to re-normalize the weights uniformly
        penalty: either enable brevity penalty or not

    Return:
        BLEU score
    """

    arg_11 = sentence_bleu([arg_0], arg_3, arg_4, arg_6, arg_9)

    arg_12 = len(arg_3)
    arg_13 = arg_12
    arg_14 = closest_ref_length([arg_0], arg_12)

    arg_15 = brevity_penalty(arg_14, arg_13)

    if arg_10 is True or arg_15 == 0:
        return arg_11

    return arg_11/arg_15


def Func(arg_0: arg_1) -> bool:
    """Verify signature certificate URL against Amazon Alexa requirements.

    Each call of Agent passes incoming utterances batch through skills filter,
    agent skills, skills processor. Batch of dialog IDs can be provided, in
    other case utterances indexes in incoming batch are used as dialog IDs.

    Args:
        url: Signature certificate URL from SignatureCertChainUrl HTTP header.

    Returns:
        result: True if verification was successful, False if not.
    """
    arg_2 = urlsplit(arg_0)

    arg_3: arg_1 = arg_2.scheme
    arg_4: arg_1 = arg_2.netloc
    arg_5: arg_1 = arg_2.path

    try:
        arg_6 = arg_2.port
    except ValueError:
        arg_6 = None

    arg_7 = (arg_3.lower() == 'https' and
              arg_4.lower().split(':')[0] == 's3.amazonaws.com' and
              arg_5.startswith('/echo.api/') and
              (arg_6 == 443 or arg_6 is None))

    return arg_7


def Func(arg_0: arg_1[arg_2.X509], arg_4: arg_2.X509) -> bool:
    """Verifies if Amazon and additional certificates creates chain of trust to a root CA.

    Args:
        certs_chain: List of pycrypto X509 intermediate certificates from signature chain URL.
        amazon_cert: Pycrypto X509 Amazon certificate.

    Returns:
        result: True if verification was successful, False if not.
    """
    arg_5 = arg_2.X509Store()

    # add certificates from Amazon provided certs chain
    for arg_6 in arg_0:
        arg_5.add_cert(arg_6)

    # add CA certificates
    arg_7 = ssl.get_default_verify_paths()

    arg_8 = arg_7.cafile
    arg_8 = Path(arg_8).resolve() if arg_8 else None

    arg_9 = arg_7.capath
    arg_9 = Path(arg_9).resolve() if arg_9 else None

    arg_10 = [arg_11 for arg_11 in arg_9.iterdir()] if arg_9 else []
    if arg_8:
        arg_10.append(arg_8)

    for arg_11 in arg_10:
        arg_11: Path
        if arg_11.is_file():
            with arg_11.open('r', encoding='ascii') as crt_f:
                arg_12 = crt_f.read()
                arg_13 = extract_certs(arg_12)
                for arg_6 in arg_13:
                    arg_5.add_cert(arg_6)

    # add CA certificates (Windows)
    arg_14 = ssl.create_default_context()
    arg_15 = arg_14.get_ca_certs(binary_form=True)
    arg_16 = '\n'.join([ssl.DER_cert_to_PEM_cert(der_cert) for der_cert in arg_15])
    arg_13 = extract_certs(arg_16)
    for arg_17 in arg_13:
        arg_5.add_cert(arg_17)

    arg_18 = arg_2.X509StoreContext(arg_5, arg_4)

    try:
        arg_18.verify_certificate()
        arg_19 = True
    except arg_2.X509StoreContextError:
        arg_19 = False

    return arg_19


def Func(arg_0: arg_1.X509, arg_3: arg_4, arg_5: arg_6) -> bool:
    """Verifies Alexa request signature.

    Args:
        amazon_cert: Pycrypto X509 Amazon certificate.
        signature: Base64 decoded Alexa request signature from Signature HTTP header.
        request_body: full HTTPS request body
    Returns:
        result: True if verification was successful, False if not.
    """
    arg_3 = base64.b64decode(arg_3)

    try:
        arg_1.verify(arg_0, arg_3, arg_5, 'sha1')
        arg_7 = True
    except arg_1.Error:
        arg_7 = False

    return arg_7


def Func(arg_0: arg_1) -> Optional[crypto.X509]:
    """Conducts series of Alexa SSL certificate verifications against Amazon Alexa requirements.

    Args:
        signature_chain_url: Signature certificate URL from SignatureCertChainUrl HTTP header.
    Returns:
        result: Amazon certificate if verification was successful, None if not.
    """
    try:
        arg_2 = requests.get(arg_0)
    except requests.exceptions.ConnectionError as e:
        log.error(f'Amazon signature chain get error: {e}')
        return None

    arg_3 = arg_2.text
    arg_4 = extract_certs(arg_3)

    arg_5: crypto.X509 = arg_4.pop(0)

    # verify signature chain url
    arg_6 = verify_sc_url(arg_0)
    if not arg_6:
        log.error(f'Amazon signature url {signature_chain_url} was not verified')

    # verify not expired
    arg_7 = not arg_5.has_expired()
    if not arg_7:
        log.error(f'Amazon certificate ({signature_chain_url}) expired')

    # verify subject alternative names
    arg_8 = verify_sans(arg_5)
    if not arg_8:
        log.error(f'Subject alternative names verification for ({signature_chain_url}) certificate failed')

    # verify certs chain
    arg_9 = Funcs_chain(arg_4, arg_5)
    if not arg_9:
        log.error(f'Certificates chain verification for ({signature_chain_url}) certificate failed')

    arg_10 = (arg_6 and arg_7 and arg_8 and arg_9)

    return arg_5 if arg_10 else None


def Func(arg_0) -> list:
        """Returns list of Telegram compatible states of the RichMessage
        instance nested controls.

        Returns:
            Func_controls: Telegram representation of RichMessage instance nested
                controls.
        """
        arg_1 = [control.Func() for control in arg_0.controls]
        return arg_1


def Func():
    """DeepPavlov console configuration utility."""
    arg_0 = parser.parse_args()
    arg_1 = get_settings_path()

    if arg_0.default:
        if populate_settings_dir(force=True):
            print(f'Populated {path} with default settings files')
        else:
            print(f'{path} is already a default settings directory')
    else:
        print(f'Current DeepPavlov settings path: {path}')


def Func(arg_0, arg_1):
    """Constructs function encapsulated in the graph."""
    @wraps(arg_0)
    def _wrapped(*arg_2, **arg_3):
        with arg_1.as_default():
            return arg_0(*arg_2, **arg_3)
    return _wrapped


def Func(arg_0: [arg_1, arg_2.ndarray], arg_4: [arg_1, arg_2.ndarray]) -> float:
    """
    Calculate Func in terms of absolute coincidence

    Args:
        y_true: array of true values
        y_predicted: array of predicted values

    Returns:
        portion of absolutely coincidental samples
    """
    arg_5 = len(arg_0)
    arg_6 = sum([y1 == y2 for y1, y2 in zip(arg_0, arg_4)])
    return arg_6 / arg_5 if arg_5 else 0


def Func() -> DefaultAgent:
    """Builds agent based on PatternMatchingSkill and HighestConfidenceSelector.

    This is agent building tutorial. You can use this .py file to check how hello-bot agent works.

    Returns:
        agent: Agent capable of handling several simple greetings.
    """
    arg_0 = PatternMatchingSkill(['Hello world'], patterns=['hi', 'hello', 'good day'])
    arg_1 = PatternMatchingSkill(['Goodbye world', 'See you around'], patterns=['bye', 'chao', 'see you'])
    arg_2 = PatternMatchingSkill(['I don\'t understand, sorry', 'I can say "Hello world"'])

    arg_3 = DefaultAgent([arg_0, arg_1, arg_2], skills_processor=HighestConfidenceSelector())

    return arg_3


def Func(arg_0, arg_1):
    """
    Takes an array of integers and transforms it
    to an array of one-hot encoded vectors
    """
    arg_2 = np.eye(arg_1, dtype=int)
    return arg_2[arg_0]


def Func(arg_0: arg_1 = False) -> arg_1:
    """
    Populate settings directory with default settings files

    Args:
        force: if ``True``, replace existing settings files with default ones

    Returns:
        ``True`` if any files were copied and ``False`` otherwise
    """
    arg_2 = False
    if _default_settings_path == _settings_path:
        return arg_2

    for arg_3 in list(_default_settings_path.glob('**/*.json')):
        arg_4 = _settings_path / arg_3.relative_to(_default_settings_path)
        if not arg_0 and arg_4.exists():
            continue
        arg_2 = True
        arg_4.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy(arg_3, arg_4)
    return arg_2


def Func(arg_0, arg_1: arg_2 = ('Optimizer',)) -> None:
        """Load model parameters from self.Func_path"""
        if not hasattr(arg_0, 'sess'):
            raise RuntimeError('Your TensorFlow model {} must'
                               ' have sess attribute!'.format(arg_0.__class__.__name__))
        arg_3 = str(arg_0.Func_path.resolve())
        # Check presence of the model files
        if tf.train.checkpoint_exists(arg_3):
            log.info('[Funcing model from {}]'.format(arg_3))
            # Exclude optimizer variables from saved variables
            arg_4 = arg_0._get_saveable_variables(arg_1)
            arg_5 = tf.train.Saver(arg_4)
            arg_5.restore(arg_0.sess, arg_3)


def Func(arg_0,
                     arg_1,
                     arg_2,
                     arg_3=None,
                     arg_4=None,
                     arg_5=None,
                     arg_6=None,
                     **arg_7):
        """
        Get train operation for given loss

        Args:
            loss: loss, tf tensor or scalar
            learning_rate: scalar or placeholder.
            clip_norm: clip gradients norm by clip_norm.
            learnable_scopes: which scopes are trainable (None for all).
            optimizer: instance of tf.train.Optimizer, default Adam.
            **kwargs: parameters passed to tf.train.Optimizer object
               (scalars or placeholders).

        Returns:
            train_op
        """
        if arg_6 is None:
            arg_8 = tf.variable_scope('Optimizer')
        else:
            arg_8 = tf.variable_scope(arg_6)
        with arg_8:
            if arg_5 is None:
                arg_9 = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
            else:
                arg_9 = []
                for arg_10 in arg_5:
                    arg_9.extend(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=arg_10))

            if arg_3 is None:
                arg_3 = tf.train.AdamOptimizer

            # For batch norm it is necessary to update running averages
            arg_11 = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
            with tf.control_dependencies(arg_11):

                def clip_if_not_none(arg_12):
                    if arg_12 is not None:
                        return tf.clip_by_norm(arg_12, arg_4)

                arg_13 = arg_3(arg_2, **arg_7)
                arg_14 = arg_13.compute_gradients(arg_1, var_list=arg_9)
                if arg_4 is not None:
                    arg_14 = [(clip_if_not_none(arg_12), var)
                                      for arg_12, var in arg_14]
                arg_15 = arg_13.apply_gradients(arg_14)
        return arg_15


def Func(arg_0, arg_1, arg_2, arg_3=True, arg_4=True):
        """
        Finds all dictionary words in d-window from word
        """
        if not all((arg_5 in arg_0.alphabet
                    or (arg_5 == " " and arg_0.allow_spaces)) for arg_5 in arg_1):
            return []
            # raise ValueError("{0} contains an incorrect symbol".format(word))
        return arg_0._trie_Func(
            arg_1, arg_2, arg_3=arg_3, arg_4=arg_4)


def Func(arg_0) -> None:
        """Initiates self-destruct timer."""
        arg_0.timer = Timer(arg_0.config['conversation_lifetime'], arg_0.self_destruct_callback)
        arg_0.timer.start()


def Func(arg_0, arg_1: arg_2) -> list:
        """Infers DeepPavlov agent with raw user input extracted from Alexa request.

        Args:
            utterance: Raw user input extracted from Alexa request.
        Returns:
            response: DeepPavlov agent response.
        """
        if arg_0.stateful:
            arg_1 = [[arg_1], [arg_0.key]]
        else:
            arg_1 = [[arg_1]]

        arg_3: list = arg_0.agent(*arg_1)

        return arg_3


def Func(arg_0, arg_1: arg_2, arg_3: arg_2) -> arg_2:
        """Populates generated response with additional data conforming Alexa response specification.

        Args:
            response: Raw user input extracted from Alexa request.
            request: Alexa request.
        Returns:
            response: Response conforming Alexa response specification.
        """
        arg_4 = deepcopy(arg_0.response_template)
        arg_4['sessionAttributes']['sessionId'] = arg_3['session']['sessionId']

        for arg_5, arg_6 in arg_4.items():
            if arg_5 not in arg_1.keys():
                arg_1[arg_5] = arg_6

        return arg_1


def Func(arg_0, arg_1: arg_2) -> arg_2:
        """Handles LaunchRequest Alexa request.

        Args:
            request: Alexa request.
        Returns:
            response: "response" part of response dict conforming Alexa specification.
        """
        arg_3 = {
            'response': {
                'shouldEndSession': False,
                'outputSpeech': {
                    'type': 'PlainText',
                    'text': arg_0.config['start_message']
                },
                'card': {
                    'type': 'Simple',
                    'content': arg_0.config['start_message']
                }
            }
        }

        arg_3 = arg_0._generate_response(arg_3, arg_1)

        return arg_3


def Func(arg_0, arg_1: arg_2) -> arg_2:
        """Handles all unsupported types of Alexa requests. Returns standard message.

        Args:
            request: Alexa request.
        Returns:
            response: "response" part of response dict conforming Alexa specification.
        """
        arg_3 = {
            'response': {
                'shouldEndSession': False,
                'outputSpeech': {
                    'type': 'PlainText',
                    'text': arg_0.config['unsupported_message']
                },
                'card': {
                    'type': 'Simple',
                    'content': arg_0.config['unsupported_message']
                }
            }
        }

        arg_3 = arg_0._generate_response(arg_3, arg_1)

        return arg_3


def Func(arg_0: arg_1[arg_2.ndarray]) -> float:
    """ Calculates perplexity by loss

    Args:
        losses: list of numpy arrays of model losses

    Returns:
        perplexity : float
    """
    arg_4 = arg_2.mean(arg_0)
    return float(arg_2.exp(arg_4))


def Func(arg_0: arg_1[arg_2, arg_3, arg_4], arg_5: arg_2 = 'infer',
                arg_6: arg_7 = False, arg_8: arg_7 = False,
                arg_9: arg_10[arg_11] = None) -> Chainer:
    """Build and return the model described in corresponding configuration file."""
    arg_0 = parse_config(arg_0)

    if arg_9:
        arg_9: list = pickle.loads(arg_9)

    if arg_8:
        deep_download(arg_0)

    import_packages(arg_0.get('metadata', {}).get('imports', []))

    arg_12 = arg_0['chainer']

    arg_13 = Chainer(arg_12['in'], arg_12['out'], arg_12.get('in_y'))

    for arg_14 in arg_12['pipe']:
        if arg_6 and ('fit_on' in arg_14 or 'in_y' in arg_14):
            try:
                arg_14['load_path'] = arg_14['save_path']
            except KeyError:
                log.warning('No "save_path" parameter for the {} component, so "load_path" will not be renewed'
                            .format(arg_14.get('class_name', arg_14.get('ref', 'UNKNOWN'))))

        if arg_9 and 'in' in arg_14:
            arg_15 = arg_9.pop(0)
        else:
            arg_15 = None

        arg_16 = from_params(arg_14, arg_5=arg_5, arg_9=arg_15)

        if 'in' in arg_14:
            arg_17 = arg_14['in']
            arg_18 = arg_14['out']
            arg_19 = arg_14.get('in_y', None)
            arg_20 = arg_14.get('main', False)
            arg_13.append(arg_16, arg_17, arg_18, arg_19, arg_20)

    return arg_13


def Func(arg_0: arg_1[arg_2, arg_3, arg_4]) -> None:
    """Start interaction with the model described in corresponding configuration file."""
    arg_5 = build_model(arg_0)

    while True:
        arg_6 = []
        for arg_7 in arg_5.in_x:
            arg_6.append((input('{}::'.format(arg_7)),))
            # check for exit command
            if arg_6[-1][0] in {'exit', 'stop', 'quit', 'q'}:
                return

        arg_8 = arg_5(*arg_6)
        if len(arg_5.out_params) > 1:
            arg_8 = zip(*arg_8)

        print('>>', *arg_8)


def Func(arg_0: arg_1[arg_2, arg_3], arg_4=False,
                arg_5: arg_6 = arg_7, arg_8: arg_6 = arg_9,
                arg_10: arg_6 = arg_11, arg_12: arg_6 = -1,
                arg_13: arg_14 = False) -> List[Tuple[List, arg_1[List, None]]]:
    """Reads input file in CONLL-U format

    Args:
        infile: a path to a file
        word_column: column containing words (default=1)
        pos_column: column containing part-of-speech labels (default=3)
        tag_column: column containing fine-grained tags (default=5)
        max_sents: maximal number of sents to read
        read_only_words: whether to read only words

    Returns:
        a list of sentences. Each item contains a word sequence and a tag sequence, which is ``None``
        in case ``read_only_words = True``
    """
    arg_15, arg_16, arg_17 = [], [], []
    if arg_4:
        arg_5, arg_13 = 0, True
    with open(arg_0, "r", encoding="utf8") as fin:
        for arg_18 in fin:
            arg_18 = arg_18.strip()
            if arg_18.startswith("#"):
                continue
            if arg_18 == "":
                if len(arg_16) > 0:
                    if arg_13:
                        arg_17 = None
                    arg_15.append((arg_16, arg_17))
                arg_17, arg_16 = [], []
                if len(arg_15) == arg_12:
                    break
                continue
            arg_19 = arg_18.split("\t")
            arg_20 = arg_19[0]
            if not arg_4 and not arg_20.isdigit():
                continue
            arg_16.append(arg_19[arg_5])
            if not arg_13:
                arg_21, arg_22 = arg_19[arg_8], arg_19[arg_10]
                arg_22 = arg_21 if arg_22 == "_" else "{},{}".format(arg_21, arg_22)
                arg_17.append(arg_22)
        if len(arg_16) > 0:
            if arg_13:
                arg_17 = None
            arg_15.append((arg_16, arg_17))
    return arg_15


def Func(arg_0: arg_1) -> Callable[..., Any]:
    """Decorator for metric registration."""
    def decorate(arg_2):
        arg_3 = arg_2.__module__ + ':' + arg_2.__name__
        if arg_0 in arg_4 and arg_4[arg_0] != arg_3:
            log.warning('"{}" is already registered as a metric name, the old function will be ignored'
                        .format(arg_0))
        arg_4[arg_0] = arg_3
        return arg_2
    return decorate


def Func(arg_0: arg_1[arg_2], arg_3: arg_1[arg_2],
                  arg_4: arg_2 = 0.9, arg_5: arg_2 = 10.0) -> arg_2:
        """
        Find the best value according to given losses

        Args:
            values: list of considered values
            losses: list of obtained loss values corresponding to `values`
            max_loss_div: maximal divergence of loss to be considered significant
            min_val_div: minimum divergence of loss to be considered significant

        Returns:
            best value divided by `min_val_div`
        """
        assert len(arg_0) == len(arg_3), "lengths of values and losses should be equal"
        arg_6 = np.argmin(arg_3)
        for arg_7 in range(arg_6 - 1, 0, -1):
            if (arg_3[arg_7] * arg_4 > arg_3[arg_6]) or\
                    (arg_0[arg_7] * arg_5 < arg_0[arg_6]):
                return arg_0[arg_7 + 1]
        return arg_0[arg_6] / arg_5


def Func(arg_0, arg_1: arg_2[arg_3], arg_4: arg_5) -> Union[arg_2[np.ndarray], np.ndarray]:
        """
        Embed one text sample

        Args:
            tokens: tokenized text sample
            mean: whether to return mean embedding of tokens per sample

        Returns:
            list of embedded tokens or array of mean values
        """
        arg_6 = []
        for arg_7 in arg_1:
            try:
                arg_8 = arg_0.tok2emb[arg_7]
            except KeyError:
                try:
                    arg_8 = arg_0._get_word_vector(arg_7)
                except KeyError:
                    arg_8 = np.zeros(arg_0.dim, dtype=np.float32)
                arg_0.tok2emb[arg_7] = arg_8
            arg_6.append(arg_8)

        if arg_4 is None:
            arg_4 = arg_0.mean

        if arg_4:
            arg_10 = [et for et in arg_6 if np.any(et)]
            if arg_10:
                return np.mean(arg_10, axis=0)
            return np.zeros(arg_0.dim, dtype=np.float32)

        return arg_6


def Func():
    """parses requirements from requirements.txt"""
    arg_0 = os.path.join(__location__, 'requirements.txt')
    with open(arg_0, encoding='utf8') as f:
        arg_1 = [line.strip() for line in f if not line.strip().startswith('#')]

    arg_2 = []
    arg_3 = []
    for arg_4 in arg_1:
        if '://' in arg_4:
            arg_3.append(arg_4)
        else:
            arg_2.append(arg_4)
    return {'install_requires': arg_2, 'dependency_links': arg_3}


def Func(arg_0, arg_1, arg_2):
    """Exports a TF-Hub module
    """

    arg_3 = make_module_spec(arg_2, str(arg_0))

    try:
        with tf.Graph().as_default():
            arg_4 = hub.Module(arg_3)

            with tf.Session() as sess:
                sess.run(tf.global_variables_initializer())
                if arg_1.exists():
                    shutil.rmtree(arg_1)
                arg_4.export(str(arg_1), sess)
    finally:
        pass


def Func() -> EcommerceAgent:
    """Make an agent

    Returns:
        agent: created Ecommerce agent
    """

    arg_0 = find_config('tfidf_retrieve')
    arg_1 = build_model(arg_0)
    arg_2 = EcommerceAgent(skills=[arg_1])
    return arg_2


def Func():
    """Parse parameters and run ms bot framework"""

    arg_0 = parser.parse_args()
    run_ms_bot_framework_server(agent_generator=make_agent,
                                app_id=arg_0.ms_id,
                                app_secret=arg_0.ms_secret,
                                stateful=True)


def Func(arg_0: [arg_1[arg_2[arg_3, arg_4]]], arg_5: arg_3, arg_6=True):
    """Download a file from URL to one or several target locations

    Args:
        dest_file_path: path or list of paths to the file destination files (including file name)
        source_url: the source URL
        force_Func: Func file if it already exists, or not

    """

    if isinstance(arg_0, list):
        arg_7 = [arg_4(path) for path in arg_0]
    else:
        arg_7 = [arg_4(arg_0).absolute()]

    if not arg_6:
        arg_8 = list(arg_7)
        arg_7 = []
        for arg_9 in arg_8:
            if arg_9.exists():
                log.info(f'File already exists in {p}')
            else:
                arg_7.append(arg_9)

    if arg_7:
        arg_10 = os.getenv('DP_CACHE_DIR')
        arg_11 = False
        if arg_10:
            arg_12 = arg_4(arg_10) / md5(arg_5.encode('utf8')).hexdigest()[:15]
            arg_11 = arg_12.exists()
        else:
            arg_12 = arg_7.pop()

        if not arg_11:
            arg_12.parent.mkdir(parents=True, exist_ok=True)

            simple_Func(arg_5, arg_12)
        else:
            log.info(f'Found cached {source_url} in {first_dest_path}')

        for arg_13 in arg_7:
            arg_13.parent.mkdir(parents=True, exist_ok=True)
            shutil.copy(arg_3(arg_12), arg_3(arg_13))


def Func(arg_0, arg_1=None):
    """Simple tar archive extractor

    Args:
        file_path: path to the tar file to be extracted
        extract_folder: folder to which the files will be extracted

    """
    arg_0 = Path(arg_0)
    if arg_1 is None:
        arg_1 = arg_0.parent
    arg_1 = Path(arg_1)
    arg_2 = tarfile.open(arg_0)
    arg_2.extractall(arg_1)
    arg_2.close()


def Func(arg_0: arg_1, arg_2: [arg_3, arg_1], arg_4=None):
    """Download and extract .tar.gz or .gz file to one or several target locations.
    The archive is deleted if extraction was successful.

    Args:
        url: URL for file downloading
        download_path: path to the directory where downloaded file will be stored
        until the end of extraction
        extract_paths: path or list of paths where contents of archive will be extracted
    """
    arg_5 = arg_3(urlparse(arg_0).path).name
    arg_2 = arg_3(arg_2)

    if arg_4 is None:
        arg_4 = [arg_2]
    elif isinstance(arg_4, list):
        arg_4 = [arg_3(path) for path in arg_4]
    else:
        arg_4 = [arg_3(arg_4)]

    arg_6 = os.getenv('DP_CACHE_DIR')
    arg_7 = False
    if arg_6:
        arg_6 = arg_3(arg_6)
        arg_8 = md5(arg_0.encode('utf8')).hexdigest()[:15]
        arg_9 = arg_6 / arg_8
        arg_10 = arg_6 / (arg_8 + '_extracted')
        arg_7 = arg_10.exists()
        if not arg_7 and not arg_9.exists():
            simple_download(arg_0, arg_9)
    else:
        arg_9 = arg_2 / arg_5
        simple_download(arg_0, arg_9)
        arg_10 = arg_4.pop()

    if not arg_7:
        log.info('Extracting {} archive into {}'.format(arg_9, arg_10))
        arg_10.mkdir(parents=True, exist_ok=True)

        if arg_5.endswith('.tar.gz'):
            untar(arg_9, arg_10)
        elif arg_5.endswith('.gz'):
            ungzip(arg_9, arg_10 / arg_3(arg_5).with_suffix('').name)
        elif arg_5.endswith('.zip'):
            with zipfile.ZipFile(arg_9, 'r') as zip_ref:
                zip_ref.extractall(arg_10)
        else:
            raise RuntimeError(f'Trying to extract an unknown type of archive {file_name}')

        if not arg_6:
            arg_9.unlink()

    for arg_11 in arg_4:
        for arg_12 in arg_10.iterdir():
            arg_13 = arg_11 / arg_12.name
            if arg_12.is_dir():
                copytree(arg_12, arg_13)
            else:
                arg_11.mkdir(parents=True, exist_ok=True)
                shutil.copy(arg_1(arg_12), arg_1(arg_13))


def Func(arg_0: arg_1, arg_2: arg_1) -> None:
    """Updates dict recursively

    You need to use this function to update dictionary if depth of editing_dict is more then 1

    Args:
        editable_dict: dictionary, that will be edited
        editing_dict: dictionary, that contains edits
    Returns:
        None
    """
    for arg_3, arg_4 in arg_2.items():
        if isinstance(arg_4, collections.Mapping):
            Func(arg_0.get(arg_3, {}), arg_4)
        else:
            arg_0[arg_3] = arg_4


def Func(arg_0, arg_1, arg_2):
    """Given a URL, set or replace a query parameter and return the modified URL.

    Args:
        url: a given  URL
        param_name: the parameter name to add
        param_value: the parameter value
    Returns:
        URL with the added parameter

    """
    arg_3, arg_4, arg_5, arg_6, arg_7 = urlsplit(arg_0)
    arg_8 = parse_qs(arg_6)

    arg_8[arg_1] = [arg_2]
    arg_9 = urlencode(arg_8, doseq=True)

    return urlunsplit((arg_3, arg_4, arg_5, arg_9, arg_7))


def Func(arg_0) -> dict:
        """Returns Amazon Alexa compatible state of the PlainText instance.

        Creating Amazon Alexa response blank with populated "outputSpeech" and
        "card sections.

        Returns:
            response: Amazon Alexa representation of PlainText state.
        """
        arg_1 = {
            'response': {
                'shouldEndSession': False,
                'outputSpeech': {
                    'type': 'PlainText',
                    'text': arg_0.content},
                'card': {
                    'type': 'Simple',
                    'content': arg_0.content
                }
            }
        }

        return arg_1


def Func(arg_0) -> dict:
        """Returns Func compatible state of the Button instance.

        Returns:
            control_Func: Json representation of Button state.
        """
        arg_1 = {}
        arg_1['name'] = arg_0.name
        arg_1['callback'] = arg_0.callback
        arg_0.control_Func['content'] = arg_1
        return arg_0.control_Func


def Func(arg_0) -> dict:
        """Returns Func compatible state of the ButtonsFrame instance.

        Returns Func compatible state of the ButtonsFrame instance including
        all nested buttons.

        Returns:
            control_Func: Json representation of ButtonsFrame state.
        """
        arg_1 = {}

        if arg_0.text:
            arg_1['text'] = arg_0.text

        arg_1['controls'] = [control.Func() for control in arg_0.content]

        arg_0.control_Func['content'] = arg_1

        return arg_0.control_Func


def Func(arg_0) -> dict:
        """Returns MS Bot Framework compatible state of the ButtonsFrame instance.

        Creating MS Bot Framework activity blank with RichCard in "attachments". RichCard
        is populated with CardActions corresponding buttons embedded in ButtonsFrame.

        Returns:
            control_json: MS Bot Framework representation of ButtonsFrame state.
        """
        arg_1 = {}

        arg_2 = [button.Func() for button in arg_0.content]
        arg_1['buttons'] = arg_2

        if arg_0.text:
            arg_1['title'] = arg_0.text

        arg_3 = [
            {
                "contentType": "application/vnd.microsoft.card.thumbnail",
                "content": arg_1
            }
        ]

        arg_4 = {}
        arg_4['type'] = 'message'
        arg_4['attachments'] = arg_3

        return arg_4


def Func(arg_0: arg_1[arg_2], arg_3: arg_1[arg_1[arg_4.ndarray]], arg_6: arg_2):
    """
    Calculates recall at k ranking metric.

    Args:
        y_true: Labels. Not used in the calculation of the metric.
        y_predicted: Predictions.
            Each prediction contains ranking score of all ranking candidates for the particular data sample.
            It is supposed that the ranking score for the true candidate goes first in the prediction.

    Returns:
        Recall at k
    """
    arg_7 = float(len(arg_3))
    arg_8 = arg_4.array(arg_3)
    arg_8 = arg_4.flip(arg_4.argsort(arg_8, -1), -1)[:, :arg_6]
    arg_9 = 0
    for arg_10 in arg_8:
        if 0 in arg_10:
            arg_9 += 1
    return float(arg_9) / arg_7


def Func(arg_0: arg_1, arg_2: arg_3[arg_4, arg_5[arg_4, arg_6, arg_7, arg_8, None]]) -> arg_1:
    """Recursively apply config's variables values to its property"""
    if isinstance(arg_0, arg_4):
        return arg_0.format(**arg_2)
    elif isinstance(arg_0, list):
        return [Func(arg_0, arg_2) for arg_0 in arg_0]
    elif isinstance(arg_0, dict):
        return {arg_9: Func(arg_10, arg_2) for arg_9, arg_10 in arg_0.items()}
    else:
        return arg_0


def Func(arg_0: arg_1[arg_2, arg_3]) -> arg_3:
    """Convert relative paths to absolute with resolving user directory."""
    return arg_3(arg_0).expanduser().resolve()


def Func(arg_0: arg_1, arg_2: arg_3 = 'infer', arg_4: arg_5 = None, **arg_6) -> Component:
    """Builds and returns the Component from corresponding dictionary of parameters."""
    # what is passed in json:
    arg_7 = {k: _resolve(v) for k, v in arg_0.items()}

    # get component by reference (if any)
    if 'ref' in arg_7:
        try:
            arg_8 = arg_13[arg_7['ref']]
            if arg_4 is not None:
                arg_8.deserialize(arg_4)
            return arg_8
        except KeyError:
            arg_9 = ConfigError('Component with id "{id}" was referenced but not initialized'
                            .format(id=arg_7['ref']))
            log.exception(arg_9)
            raise arg_9

    elif 'config_path' in arg_7:
        from deeppavlov.core.commands.infer import build_model
        arg_10 = arg_13.copy()
        arg_13.clear()
        arg_11 = parse_config(expand_path(arg_7['config_path']))
        arg_12 = build_model(arg_11, arg_4=arg_4)
        arg_13.clear()
        arg_13.update(arg_10)
        try:
            arg_13[arg_7['id']] = arg_12
        except KeyError:
            pass
        return arg_12

    arg_14 = arg_7.pop('class_name', None)
    if not arg_14:
        arg_9 = ConfigError('Component config has no `class_name` nor `ref` fields')
        log.exception(arg_9)
        raise arg_9
    arg_15 = get_model(arg_14)

    # find the submodels params recursively
    arg_7 = {k: _init_param(v, arg_2) for k, v in arg_7.items()}

    try:
        arg_16 = inspect.getfullargspec(arg_15)
        if 'mode' in arg_16.args+arg_16.kwonlyargs or arg_16.varkw is not None:
            arg_6['mode'] = arg_2

        arg_8 = arg_15(**dict(arg_7, **arg_6))
        try:
            arg_13[arg_7['id']] = arg_8
        except KeyError:
            pass
    except Exception:
        log.exception("Exception in {}".format(arg_15))
        raise

    if arg_4 is not None:
        arg_8.deserialize(arg_4)
    return arg_8


def Func(arg_0) -> None:
        """Thread Func method implementation."""
        while True:
            arg_1 = arg_0.input_queue.get()
            arg_2 = arg_0._handle_request(arg_1)
            arg_0.output_queue.put(arg_2)


def Func(arg_0, arg_1: arg_2) -> None:
        """Deletes Conversation instance.

        Args:
            conversation_key: Conversation key.
        """
        if arg_1 in arg_0.conversations.keys():
            del arg_0.conversations[arg_1]
            log.info(f'Deleted conversation, key: {conversation_key}')


def Func(arg_0) -> None:
        """Conducts cleanup of periodical certificates with expired validation."""
        arg_0.timer = Timer(REFRESH_VALID_CERTS_PERIOD_SECS, arg_0.Func)
        arg_0.timer.start()

        arg_2 = []

        for arg_3, arg_4 in arg_0.valid_certificates.items():
            arg_4: ValidatedCert = arg_4
            arg_5: datetime = arg_4.expiration_timestamp
            if datetime.utcnow() > arg_5:
                arg_2.append(arg_3)

        for arg_6 in arg_2:
            del arg_0.valid_certificates[arg_6]
            log.info(f'Validation period of {expired_cert_url} certificate expired')


def Func(arg_0, arg_1: arg_2, arg_3: arg_2, arg_4: arg_5) -> bool:
        """Conducts series of Alexa request verifications against Amazon Alexa requirements.

        Args:
            signature_chain_url: Signature certificate URL from SignatureCertChainUrl HTTP header.
            signature: Base64 decoded Alexa request signature from Signature HTTP header.
            request_body: full HTTPS request body
        Returns:
            result: True if verification was successful, False if not.
        """
        if arg_1 not in arg_0.valid_certificates.keys():
            arg_6: X509 = verify_cert(arg_1)
            if arg_6:
                arg_7: timedelta = arg_0.config['amazon_cert_lifetime']
                arg_8 = datetime.utcnow() + arg_7
                arg_9 = ValidatedCert(cert=arg_6, arg_8=arg_8)
                arg_0.valid_certificates[arg_1] = arg_9
                log.info(f'Certificate {signature_chain_url} validated')
            else:
                log.error(f'Certificate {signature_chain_url} validation failed')
                return False
        else:
            arg_9: ValidatedCert = arg_0.valid_certificates[arg_1]
            arg_6: X509 = arg_9.cert

        if verify_signature(arg_6, arg_3, arg_4):
            arg_11 = True
        else:
            log.error(f'Failed signature verification for request: {request_body.decode("utf-8", "replace")}')
            arg_11 = False

        return arg_11


def Func(arg_0):
        """
        Extract full regularization path explored during lambda search from glm model.

        :param model: source lambda search model
        """
        arg_1 = h2o.api("GET /3/GetGLMRegPath", data={"model": arg_0._model_json["model_id"]["name"]})
        arg_2 = arg_1.pop("coefficient_names")
        arg_3 = {
            "lambdas": arg_1["lambdas"],
            "explained_deviance_train": arg_1["explained_deviance_train"],
            "explained_deviance_valid": arg_1["explained_deviance_valid"],
            "coefficients": [dict(zip(arg_2, y)) for y in arg_1["coefficients"]],
        }
        if "coefficients_std" in arg_1:
            arg_3["coefficients_std"] = [dict(zip(arg_2, y)) for y in arg_1["coefficients_std"]]
        return arg_3


def Func(arg_0, arg_1, arg_2=.5):
        """
        Create a custom GLM model using the given coefficients.

        Needs to be passed source model trained on the dataset to extract the dataset information from.

        :param model: source model, used for extracting dataset information
        :param coefs: dictionary containing model coefficients
        :param threshold: (optional, only for binomial) decision threshold used for classification
        """
        arg_3 = h2o.api(
            "POST /3/MakeGLMModel",
            data={"model": arg_0._model_json["model_id"]["name"],
                  "names": list(arg_1.keys()),
                  "beta": list(arg_1.values()),
                  "threshold": arg_2}
        )
        arg_4 = H2OGeneralizedLinearEstimator()
        arg_4._resolve_model(arg_3["model_id"]["name"], arg_3)
        return arg_4


def Func(arg_0):
        """
        Determine if the H2O cluster is running or not.

        :returns: True if the cluster is up; False otherwise
        """
        try:
            if h2o.connection().local_server and not h2o.connection().local_server.Func(): return False
            h2o.api("GET /")
            return True
        except (H2OConnectionError, H2OServerError):
            return False


def Func(arg_0):
        """List all jobs performed by the cluster."""
        arg_1 = h2o.api("GET /3/Jobs")
        arg_2 = [["type"], ["dest"], ["description"], ["status"]]
        for arg_3 in arg_1["jobs"]:
            arg_4 = arg_3["dest"]
            arg_2[0].append(arg_0._translate_job_type(arg_4["type"]))
            arg_2[1].append(arg_4["name"])
            arg_2[2].append(arg_3["description"])
            arg_2[3].append(arg_3["status"])
        return arg_2


def Func(arg_0):
        """Return the list of all known timezones."""
        from h2o.expr import ExprNode
        return h2o.H2OFrame._expr(expr=ExprNode("listTimeZones"))._frame()


def Func(arg_0, arg_1):
        """
        Update information in this object from another H2OCluster instance.

        :param H2OCluster other: source of the new information for this object.
        """
        arg_0._props = arg_1._props
        arg_0._retrieved_at = arg_1._retrieved_at
        arg_1._props = {}
        arg_1._retrieved_at = None


def Func(arg_0):
        """
        Parameters for metalearner algorithm

        Type: ``dict``  (default: ``None``).
        Example: metalearner_gbm_params = {'max_depth': 2, 'col_sample_rate': 0.3}
        """
        if arg_0._parms.get("Func") != None:
            arg_1 =  ast.literal_eval(arg_0._parms.get("Func"))
            for arg_2 in arg_1:
                if len(arg_1[arg_2]) == 1: #single parameter
                    arg_1[arg_2] = arg_1[arg_2][0]
            return arg_1
        else:
            return arg_0._parms.get("Func")


def Func(arg_0, arg_1, arg_2, arg_3=10, arg_4=0.5):
        '''Repeatedly test a function waiting for it to return True.

        Arguments:
        test_func      -- A function that will be run repeatedly
        error          -- A function that will be run to produce an error message
                          it will be called with (node, timeTakenSecs, numberOfRetries)
                    OR
                       -- A string that will be interpolated with a dictionary of
                          { 'timeTakenSecs', 'numberOfRetries' }
        timeoutSecs    -- How long in seconds to keep trying before declaring a failure
        retryDelaySecs -- How long to wait between retry attempts
        '''
        arg_5 = time.time()
        arg_6 = 0
        while h2o_args.no_timeout or (time.time() - arg_5 < arg_3):
            if arg_1(arg_0, tries=arg_6, arg_3=arg_3):
                break
            time.sleep(arg_4)
            arg_6 += 1
            # hey, check the sandbox if we've been waiting a long time...rather than wait for timeout
            # to find the badness?. can check_sandbox_for_errors at any time
            if ((arg_6 % 50) == 0):
                check_sandbox_for_errors(python_test_name=h2o_args.python_test_name)

        else:
            arg_7 = time.time() - arg_5
            if isinstance(arg_2, type('')):
                raise Exception('%s failed after %.2f seconds having retried %d times' % (
                    arg_2, arg_7, arg_6))
            else:
                arg_8 = arg_2(arg_0, arg_7, arg_6)
                raise Exception(arg_8)


def Func(arg_0, arg_1, arg_2="C1", arg_3=10, **arg_4):
    '''
    Return the Func for a single column for a single Frame in the h2o cluster.
    '''
    arg_5 = {
        # 'offset': 0,
        # 'len': 100
    }
    h2o_methods.check_params_update_kwargs(arg_5, arg_4, 'Func', True)

    arg_6 = arg_0.do_json_request('3/Frames.json/%s/columns/%s/Func' % (arg_1, arg_2), timeout=arg_3, params=arg_5)
    h2o_sandbox.check_sandbox_for_errors()
    return arg_6


def Func(arg_0, arg_1, arg_2=True, arg_3=60, **arg_4):
    '''
    Delete a frame on the h2o cluster, given its key.
    '''
    assert arg_1 is not None, '"key" parameter is null'

    arg_5 = arg_0.do_json_request('/3/Frames.json/' + arg_1, cmd='delete', timeout=arg_3)

    # TODO: look for what?
    if not arg_2 and 'f00b4r' in arg_5:
        raise ValueError('Frame key not found: ' + arg_1)
    return arg_5


def Func(arg_0, arg_1=None, arg_2=10, **arg_3):
    '''
    Return a model builder or all of the model builders known to the
    h2o cluster.  The model builders are contained in a dictionary
    called "Func" at the top level of the result.  The
    dictionary maps algorithm names to parameters lists.  Each of the
    parameters contains all the metdata required by a client to
    present a model building interface to the user.

    if parameters = True, return the parameters?
    '''
    arg_4 = {}
    h2o_methods.check_params_update_kwargs(arg_4, arg_3, 'Func', False)

    arg_5 = '3/ModelBuilders.json'
    if arg_1:
        arg_5 += "/" + arg_1

    arg_6 = arg_0.do_json_request(arg_5, timeout=arg_2, params=arg_4)
    # verboseprint(request, "result:", dump_json(result))
    h2o_sandbox.check_sandbox_for_errors()
    return arg_6


def Func(arg_0, arg_1, arg_2, arg_3, arg_4=60, **arg_5):
    '''
    Check a dictionary of model builder parameters on the h2o cluster
    using the given algorithm and model parameters.
    '''
    assert arg_1 is not None, '"algo" parameter is null'
    # Allow this now: assert training_frame is not None, '"training_frame" parameter is null'
    assert arg_3 is not None, '"parameters" parameter is null'

    arg_6 = arg_0.model_builders(arg_4=arg_4)
    assert arg_6 is not None, "/ModelBuilders REST call failed"
    assert arg_1 in arg_6['model_builders']
    arg_7 = arg_6['model_builders'][arg_1]

    # TODO: test this assert, I don't think this is working. . .
    if arg_2 is not None:
        arg_8 = arg_0.frames(key=arg_2)
        assert arg_8 is not None, "/Frames/{0} REST call failed".format(arg_2)

        arg_9 = arg_8['frames'][0]['key']['name']
        assert arg_9==arg_2, \
            "/Frames/{0} returned Frame {1} rather than Frame {2}".format(arg_2, arg_9, arg_2)

        arg_3['training_frame'] = arg_2

    # TODO: add parameter existence checks
    # TODO: add parameter value validation

    # FIX! why ignoreH2oError here?
    arg_10 = arg_0.do_json_request('/3/ModelBuilders.json/' + arg_1 + "/parameters", cmd='post',
        timeout=arg_4, postData=arg_3, ignoreH2oError=True, noExtraErrorCheck=True)

    verboseprint("model parameters validation: " + repr(arg_10))
    return arg_10


def Func(arg_0, arg_1, arg_2, arg_3=60, **arg_4):
    '''
    Score a model on the h2o cluster on the given Frame and return only the model metrics.
    '''
    assert arg_1 is not None, '"model" parameter is null'
    assert arg_2 is not None, '"frame" parameter is null'

    arg_5 = arg_0.models(key=arg_1, arg_3=arg_3)
    assert arg_5 is not None, "/Models REST call failed"
    assert arg_5['models'][0]['model_id']['name'] == arg_1, "/Models/{0} returned Model {1} rather than Model {2}".format(arg_1, arg_5['models'][0]['key']['name'], arg_1)

    # TODO: test this assert, I don't think this is working. . .
    arg_6 = arg_0.frames(key=arg_2)
    assert arg_6 is not None, "/Frames/{0} REST call failed".format(arg_2)

    print "frames:", dump_json(arg_6)
    # is the name not there?
    # assert frames['frames'][0]['model_id']['name'] == frame, "/Frames/{0} returned Frame {1} rather than Frame {2}".format(frame, models['models'][0]['key']['name'], frame)

    arg_7 = arg_0.do_json_request('/3/ModelMetrics.json/models/' + arg_1 + '/frames/' + arg_2, cmd='post', timeout=arg_3)

    arg_8 = arg_7['model_metrics'][0]
    verboseprint("model metrics: " + repr(arg_8))
    h2o_sandbox.check_sandbox_for_errors()
    return arg_8


def Func(arg_0, arg_1=60, **arg_2):
    '''
    ModelMetrics list.
    '''
    arg_3 = arg_0.do_json_request('/3/ModelMetrics.json', cmd='get', timeout=arg_1)
    h2o_sandbox.check_sandbox_for_errors()
    return arg_3


def Func(arg_0, arg_1, arg_2, arg_3=True, arg_4=None):
    '''Create a new reservation for count instances'''

    arg_5 = inheritparams(arg_1, EC2_API_RUN_INSTANCE)
    arg_5.setdefault('min_count', arg_0)
    arg_5.setdefault('max_count', arg_0)

    arg_6 = None
    arg_7 = ec2_connect(arg_2)
    try:
        arg_6 = arg_7.Func(**arg_5)
        log('Reservation: {0}'.format(arg_6.id))
        log('Waiting for {0} EC2 instances {1} to come up, this can take 1-2 minutes.'.format(len(arg_6.instances), arg_6.instances))
        arg_8 = time.time()
        time.sleep(1)
        for arg_9 in arg_6.instances:
            while arg_9.update() == 'pending':
               time.sleep(1)
               h2o_cmd.dot()

            if not arg_9.state == 'running':
                raise Exception('\033[91m[ec2] Error waiting for running state. Instance is in state {0}.\033[0m'.format(arg_9.state))

        log('Instances started in {0} seconds'.format(time.time() - arg_8))
        log('Instances: ')
        for arg_10 in arg_6.instances: log("   {0} ({1}) : public ip: {2}, private ip: {3}".format(arg_10.public_dns_name, arg_10.id, arg_10.ip_address, arg_10.private_ip_address))

        if arg_3:
            # kbn: changing to private address, so it should fail if not in right domain
            # used to have the public ip address
            wait_for_ssh([ arg_11.private_ip_address for arg_11 in arg_6.instances ])

        # Tag instances
        try:
            if arg_4:
                arg_7.create_tags([arg_11.id for arg_11 in arg_6.instances], arg_4)
        except:
            warn('Something wrong during tagging instances. Exceptions IGNORED!')
            print sys.exc_info()
            pass

        return arg_6
    except:
        print "\033[91mUnexpected error\033[0m :", sys.exc_info()
        if arg_6:
            terminate_reservation(arg_6, arg_2)
        raise


def Func(arg_0, arg_1):
    '''terminate all the instances given by its ids'''
    if not arg_0: return
    arg_2 = ec2_connect(arg_1)
    log("Terminating instances {0}.".format(arg_0))
    arg_2.Func(arg_0)
    log("Done")


def Func(arg_0, arg_1):
    '''Reboot all the instances given by its ids'''
    if not arg_0: return
    arg_2 = ec2_connect(arg_1)
    log("Rebooting instances {0}.".format(arg_0))
    arg_2.Func(arg_0)
    log("Done")