import numpy as np
import tensorflow as tf


class Rnn_Engine(object):

    def __init__(self):
        self.args = get_args()
        if 'true' == self.args.training:
            self.batch_size = 1
            self.seq_length = 1

    def train(self):
        build_model()

    def build_model(self):
        print('build_model')

    def get_args(self):
        parser = argparse.ArgumentParser(formatter_class=argparse.
            ArgumentDefaultsHelpFormatter)
        parser.add_argument('--data_dir', type=str, default='datasets',
            help='data directory containing input.txt')
        parser.add_argument('--training', type=str, default='true', help=
            'data directory containing input.txt')
        parser.add_argument('--model', type=str, default='lstm', help=
            'data directory containing input.txt')
        return parser.parse_args()

ece675f28db9439edaf295b4afca5bbdaec544b3
<mask token>


class Rnn_Engine(object):
    <mask token>
    <mask token>

    def build_model(self):
        print('build_model')

    def get_args(self):
        parser = argparse.ArgumentParser(formatter_class=argparse.
            ArgumentDefaultsHelpFormatter)
        parser.add_argument('--data_dir', type=str, default='datasets',
            help='data directory containing input.txt')
        parser.add_argument('--training', type=str, default='true', help=
            'data directory containing input.txt')
        parser.add_argument('--model', type=str, default='lstm', help=
            'data directory containing input.txt')
        return parser.parse_args()

<mask token>


class Rnn_Engine(object):
    <mask token>

    def train(self):
        build_model()

    def build_model(self):
        print('build_model')

    def get_args(self):
        parser = argparse.ArgumentParser(formatter_class=argparse.
            ArgumentDefaultsHelpFormatter)
        parser.add_argument('--data_dir', type=str, default='datasets',
            help='data directory containing input.txt')
        parser.add_argument('--training', type=str, default='true', help=
            'data directory containing input.txt')
        parser.add_argument('--model', type=str, default='lstm', help=
            'data directory containing input.txt')
        return parser.parse_args()

<mask token>


class Rnn_Engine(object):

    def __init__(self):
        self.args = get_args()
        if 'true' == self.args.training:
            self.batch_size = 1
            self.seq_length = 1

    def train(self):
        build_model()

    def build_model(self):
        print('build_model')

    def get_args(self):
        parser = argparse.ArgumentParser(formatter_class=argparse.
            ArgumentDefaultsHelpFormatter)
        parser.add_argument('--data_dir', type=str, default='datasets',
            help='data directory containing input.txt')
        parser.add_argument('--training', type=str, default='true', help=
            'data directory containing input.txt')
        parser.add_argument('--model', type=str, default='lstm', help=
            'data directory containing input.txt')
        return parser.parse_args()

import numpy as np
import tensorflow as tf


class Rnn_Engine(object):

    def __init__(self):
        self.args = get_args()
        if 'true' == self.args.training:
            self.batch_size = 1
            self.seq_length = 1

    def train(self):
        build_model()

    def build_model(self):
        print('build_model')

    def get_args(self):
        parser = argparse.ArgumentParser(formatter_class=argparse.
            ArgumentDefaultsHelpFormatter)
        parser.add_argument('--data_dir', type=str, default='datasets',
            help='data directory containing input.txt')
        parser.add_argument('--training', type=str, default='true', help=
            'data directory containing input.txt')
        parser.add_argument('--model', type=str, default='lstm', help=
            'data directory containing input.txt')
        return parser.parse_args()
