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class Path:
    _offset_to_distance: dict[tuple[int, int], float] = {
        (0, 0): 0,
        (0, 1): 1,
        (0, -1): 1,
        (1, 0): 1,
        (1, 1): 2 ** 0.5,
        (1, -1): 2 ** 0.5,
        (-1, 0): 1,
        (-1, 1): 2 ** 0.5,

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def getCtAugmentedNodule(augmentation_dict, series_uid, center_xyz, width_mm, voxels_int, maxWidth_mm=32.0, use_cache=True):
    assert width_mm <= maxWidth_mm

    if use_cache:
        cubic_chunk, center_irc = getCtCubicChunk(series_uid, center_xyz, maxWidth_mm)
    else:
        ct = getCt(series_uid)
        ct_chunk, center_irc = ct.getCubicInputChunk(center_xyz, maxWidth_mm)

    slice_list = []

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import math
import random

import numpy as np
import scipy.ndimage

import torch

import pytest

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import math
import random
import warnings

import numpy as np
import scipy.ndimage

import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable

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from __future__ import print_function

import torch

from torch.nn.parallel.data_parallel import DataParallel
from torchvision.models import resnet18
from torch.autograd import Variable

model_list = [DataParallel(resnet18(), output_device=(i % torch.cuda.device_count())).cuda() for i in range(20)]

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import itertools

def attack(n, rerolls=0, focus=False, r='', p=1.0):
    s = 'CHHHffxx'
    focus = 'h' if focus else None
    result_dict = dice(s, n, r, p)
    result_dict = reroll(result_dict, s, rerolls, focus)
    #print 'attack', n, rerolls, focus, repr(r), p, result_dict

    result_dict = usefocus(result_dict, focus)

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