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channel = new FileInputStream(dbFilePath).getChannel();
            ByteBuffer buffer = channel.map(FileChannel.MapMode.READ_ONLY, 0, channel.size());
            try {
                while (buffer.remaining() > 0) {
                    int keySize = buffer.getInt();
                    byte[] key = new byte[keySize];
                    buffer.get(key);
                    int valueSize = buffer.getInt();
                    byte[] value = new byte[valueSize];
                    buffer.get(value);

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#! /usr/bin/env python3

import sys
import random

width = int(sys.argv[1])
height = int(sys.argv[2])

for i in range(0, width):
    for j in range(0, height):

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#! /usr/bin/env python3

import sys

def read_matrix(path):
    A = []
    with open(path) as f:
        content = f.readlines()
        for line in content:
            idx1, idx2, other = line.split()

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with g.as_default():
    print([v.name for v in tf.all_variables()])
    W = [v for v in tf.all_variables() if v.name == "Conv/weights:0"][0]
    W_val = sess.run(W)
    fig, axes = plt.subplots(5, 5, figsize=(15, 15))
    for i in range(5):
        for j in range(5):
            axes[i, j].imshow(W_val[:, :, 0, i * 5 + j], interpolation='nearest', cmap='gray')
    plt.show()

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from agentnet.utils.persistence import save,load
def build_pretrained_spaceinvaders(observation_reshape):
    """ a smaller version of DQN pre-trained for ~2 hours on GPU """
    assert tuple(observation_reshape.output_shape) == (None, 3, 105, 80)
    
    #main neural network body
    conv0 = Conv2DLayer(observation_reshape,16,filter_size=(8,8),stride=(4,4),name='conv0')
    conv1 = Conv2DLayer(conv0,32,filter_size=(4,4),stride=(2,2),name='conv1')
    dense0 = DenseLayer(conv1,256,name='dense',nonlinearity=lasagne.nonlinearities.tanh)
    

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\documentclass[a4paper,12pt]{article}

\usepackage[T2A]{fontenc}
%\usepackage[utf8]{inputenc}   % older versions of ucs package
\usepackage[utf8x]{inputenc}  % more recent versions (at least>=2004-17-10)
\usepackage[russian]{babel}
\usepackage{amsmath}

\sloppy                         % Hyphenation is a problem..

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import numpy as np
import matplotlib.pyplot as plt
from IPython.display import Audio
from scipy.signal import spectrogram

sample_rate = 8000
t = np.arange(0, 10, 1.0/sample_rate)
x = t % (2 / sample_rate)

plt.figure(figsize=(16, 6), dpi=200)

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import sys, re, pymystem3

mystem = pymystem3.Mystem()

def stem(s):
  return [(e['text'].strip(), \
    e['analysis'][0]['lex'] \
       if 'analysis' in e and len(e['analysis']) > 0 else '', \
    re.match('^([A-Z]+)', e['analysis'][0]['gr']).group(0) \
       if 'analysis' in e and len(e['analysis']) > 0 else '', \

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with tf.name_scope("subgraph_1"):
  with tf.variable_scope(tf.get_variable_scope(), resue=False):
    y_1 = output(x_1)

with tf.name_scope("subgraph_2"):
  with tf.variable_scope(tf.get_variable_scope(), reuse=True):
    y_2 = output(x_2)

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"""
Based on example here: https://gist.github.com/awni/56369a90d03953e370f3964c826ed4b0
"""

import numpy as np
import math
import collections

NEG_INF = -float("inf")