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amount,duration,rate,down_payment
10000,36,0.08,20000
200000,12,0.1,
628400,120,0.12,100000
4637400,240,0.06,
42900,90,0.07,8900
916000,16,0.13,
45230,48,0.08,4300
991360,99,0.08,
423000,27,0.09,47200

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import android.content.pm.PackageManager;
import android.os.Build;

/**
 * {@link PermissionHandler} provides a simple API to request Android permissions
 * at runtime. The class exposes one static method: {@link #request} which the
 * requesting activity should call to request or check for permission. The requesting
 * activity must implement {@link PermissionActivity}.
 */
public class PermissionHandler {

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#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
"""
Run inference for pre-processed data with a trained model.
"""

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from fastai.conv_learner import ConvLearner, num_cpus, accuracy

def get_learner(arch, bs):
    """Create a FastAI learner using the given model"""
    data = get_data(bs, num_cpus())
    learn = ConvLearner.from_model_data(arch.cuda(), data)
    learn.crit = nn.CrossEntropyLoss()
    learn.metrics = [accuracy]
    return learn

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import torch.nn as nn
import torch.nn.functional as F

def conv_2d(ni, nf, stride=1, ks=3):
    return nn.Conv2d(in_channels=ni, out_channels=nf, 
                     kernel_size=ks, stride=stride, 
                     padding=ks//2, bias=False)

def bn_relu_conv(ni, nf):
    return nn.Sequential(nn.BatchNorm2d(ni), 

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import Loadable from "react-loadable";
import Loading from "./Loading";

const AsyncSettingsPage = Loadable({
  loader: () => import("./SettingsPage"),
  loading: Loading
});

export { AsyncSettingsPage };

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#!/usr/bin/python

# Note to Kagglers: This script will not run directly in Kaggle kernels. You
# need to download it and run it on your local machine.

# Downloads images from the Google Landmarks dataset using multiple threads.
# Images that already exist will not be downloaded again, so the script can
# resume a partially completed download. All images will be saved in the JPG
# format with 90% compression quality.

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