<commit_before># Original author: yasunorikudo
# (https://github.com/yasunorikudo/chainer-ResNet)

import chainer
import chainer.functions as F
from chainer import initializers
import chainer.links as L


class BottleNeckA(chainer.Chain):

    def __init__(self, in_size, ch, out_size, stride=2):
        super(BottleNeckA, self).__init__(
            conv1=L.Convolution2D(in_size, ch, 1, stride, 0,
                                  initialW=initializers.HeNormal()),
            bn1=L.BatchNormalization(ch),
            conv2=L.Convolution2D(ch, ch, 3, 1, 1,
                                  initialW=initializers.HeNormal(),
                                  nobias=True),
            bn2=L.BatchNormalization(ch),
            conv3=L.Convolution2D(ch, out_size, 1, 1, 0,
                                  initialW=initializers.HeNormal(),
                                  nobias=True),
            bn3=L.BatchNormalization(out_size),

            conv4=L.Convolution2D(in_size, out_size, 1, stride, 0,
                                  initialW=initializers.HeNormal(),
                                  nobias=True),
            bn4=L.BatchNormalization(out_size),
        )

    def __call__(self, x):
        h1 = F.relu(self.bn1(self.conv1(x)))<commit_msg>Fix wrong modification<commit_after># Original author: yasunorikudo
# (https://github.com/yasunorikudo/chainer-ResNet)

import chainer
import chainer.functions as F
from chainer import initializers
import chainer.links as L


class BottleNeckA(chainer.Chain):

    def __init__(self, in_size, ch, out_size, stride=2):
        super(BottleNeckA, self).__init__(
            conv1=L.Convolution2D(in_size, ch, 1, stride, 0,
                                  initialW=initializers.HeNormal()
                                  nobias=True),
            bn1=L.BatchNormalization(ch),
            conv2=L.Convolution2D(ch, ch, 3, 1, 1,
                                  initialW=initializers.HeNormal(),
                                  nobias=True),
            bn2=L.BatchNormalization(ch),
            conv3=L.Convolution2D(ch, out_size, 1, 1, 0,
                                  initialW=initializers.HeNormal(),
                                  nobias=True),
            bn3=L.BatchNormalization(out_size),

            conv4=L.Convolution2D(in_size, out_size, 1, stride, 0,
                                  initialW=initializers.HeNormal(),
                                  nobias=True),
            bn4=L.BatchNormalization(out_size),
        )

    def __call__(self, x):
        h1 = F.relu(self.bn1(self.conv1(x)))
{'fraction_non_alphanumeric': 0.10150521034349672, 'fraction_numerical': 0.020069471246622925, 'mean_word_length': 1.6832298136645962, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 15, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1980045', 'n_tokens_mistral': 822, 'n_tokens_neox': 768, 'n_words': 132}