class Bottleneck(nn.Module):
    expansion = 4 ------ ResNet에서 병목 블록을 정의하기 위한 하이퍼파라미터입니다.

    def __init__(self, in_channels, out_channels, stride=1, downsample=False):
        super().__init__()
         self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False) ------ 1×1 합성곱층
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) ------ 3×3 합성곱층
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, self.expansion*out_channels, kernel_size=1, stride=1, bias=False) ------ 1×1 합성곱층, 또한 다음 계층의 입력 채널 수와 일치하도록 self.expansion*out_channels를 합니다.
        self.bn3 = nn.BatchNorm2d(self.expansion*out_channels)
        self.relu = nn.ReLU(inplace=True)

        if downsample:
            conv = nn.Conv2d(in_channels, self.expansion*out_channels, kernel_size=1, stride=stride, bias=False)
            bn = nn.BatchNorm2d(self.expansion*out_channels)
            downsample = nn.Sequential(conv, bn)
        else:
            downsample = None
            self.downsample = downsample

    def forward(self, x):
        i = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv3(x)
        x = self.bn3(x)

        if self.downsample is not None:
            i = self.downsample(i)

        x += i
        x = self.relu(x)
        return x