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"""From https://gist.github.com/shivakar/82ac5c9cb17c95500db1906600e5e1ea"""

import os
from SimpleHTTPServer import SimpleHTTPRequestHandler
import sys
import BaseHTTPServer


class RangeHTTPRequestHandler(SimpleHTTPRequestHandler):
    """RangeHTTPRequestHandler is a SimpleHTTPRequestHandler

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from rl.callbacks import TrainIntervalLogger
from tqdm import tqdm_notebook
import timeit

class TrainIntervalLoggerTQDMNotebook(TrainIntervalLogger):
    """TrainIntervalLogger using tqdm_notebook for jupyter-notebook."""
    def reset(self):
        self.interval_start = timeit.default_timer()
        self.metrics = []
        self.infos = []

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from rl.callbacks import Callback
from rl.callbacks import TrainIntervalLogger
from keras import backend as K
import warnings
from tqdm import tqdm_notebook
import timeit
import numpy as np

class TrainIntervalLoggerTQDMNotebook(TrainIntervalLogger):
    """TrainIntervalLogger for keras-rl using tqdm_notebook for jupyter-notebook."""

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import numpy as np
from matplotlib import pyplot as plt
def subimshow(image, title=''):
    """
    Show each band seperately.
    
    The shape si a square or rectangle of images
    
    image: array of shape (channel, height, width)
    

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# summarize model
from collections import OrderedDict
import pandas as pd

import torch
from torch import nn
from torch.autograd import Variable

class TorchSummarizeDf(object):
    def __init__(self, model, weights=False, input_shape=True, nb_trainable=False, debug=False):

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