import sys
_module = sys.modules[__name__]
del sys
bots = _module
generate_data = _module
dataloader = _module
evaluate = _module
options = _module
train = _module
world = _module

from _paritybench_helpers import _mock_config, patch_functional
from unittest.mock import mock_open, MagicMock
from torch.autograd import Function
from torch.nn import Module
import abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings
import numpy as np
from torch import Tensor
patch_functional()
open = mock_open()
yaml = logging = sys = argparse = MagicMock()
ArgumentParser = argparse.ArgumentParser
_global_config = args = argv = cfg = config = params = _mock_config()
argparse.ArgumentParser.return_value.parse_args.return_value = _global_config
yaml.load.return_value = _global_config
sys.argv = _global_config
__version__ = '1.0.0'
xrange = range
wraps = functools.wraps


import math


import torch


from torch import nn


from torch.autograd import Variable


from torch.autograd import backward as autograd_backward


from functools import reduce


import random


from torch.utils.data import Dataset


from torch.utils.data import DataLoader


import numpy as np


from torch import optim


def xavier_init(module):
    """Xavier initializer for module parameters."""
    for parameter in module.parameters():
        if len(parameter.data.shape) == 1:
            parameter.data.fill_(0)
        else:
            fan_in = parameter.data.size(0)
            fan_out = parameter.data.size(1)
            parameter.data.normal_(0, math.sqrt(2 / (fan_in + fan_out)))
    return module

