qbx2

from collections import Counter
import itertools

lines = '''스윕 이그나이트 이럽션
이그나이트 이프리트 리젼
이럽션 이그나이트 이프리트
이그나이트 스윕 이럽션
이그나이트 스윕 이프리트
이그나이트 리젼 이럽션
이그나이트 이럽션 이프리트

qbx2

class FileBackedCircularBuffer:
    def __init__(self, cls, maxlen):
        f = tempfile.TemporaryFile()
        filesize = maxlen * cls.format.size
        f.truncate(filesize)
        self.buffer = mmap.mmap(f.fileno(), filesize)
        self.cls = cls
        self.maxlen = maxlen
        self.start_index = 0  # inclusive
        self.end_index = 0    # exclusive

qbx2

#!/bin/bash
set -ex

# Do argument checks
if [ ! "$#" -ge 1 ]; then
    echo "Usage: $0 {ip_addr}"
    exit 1
fi

IP_ADDRESS=$1

qbx2

#!/bin/bash
snap install microk8s --classic

qbx2

#!/bin/sh

# Do argument checks
if [ ! "$#" -ge 1 ]; then
    echo "Usage: $0 {size}"
    echo "Example: $0 4G"
    echo "Usage: $0 {size} {swappiness}"
    exit 1
fi

qbx2

'''
Use the IP_BOUND_IF socket option to bind to a specific network interface on macOS.
'''
import socket

IP_BOUND_IF = 25

s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.setsockopt(socket.IPPROTO_IP, IP_BOUND_IF, socket.if_nametoindex('en0'))

qbx2

import torch


def calculate_loss(x, y):
    clip_range = 100.
    clipped = y + (x - y).clamp(-clip_range, clip_range)
    l_vf = 0.5 * torch.max((clipped - y) ** 2, (x - y) ** 2).mean()
    return l_vf

qbx2

from collections import defaultdict
metrics = defaultdict(float)
num_metrics = 0

# training loop
for xs, ys in training_dataloader:
  batch_size = xs.size(0)
  loss = criterion(...)
  metrics['loss'] += float(loss) * batch_size
  num_metrics += batch_size

qbx2

-----BEGIN CERTIFICATE-----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qbx2

import tensorflow as tf
import random


W = tf.get_variable('W', shape=[1, 4])
ph_y = tf.placeholder(tf.int32, [1])  # labels

prob = tf.nn.softmax(W)

loss = tf.reduce_mean(