def _make_layer(self, block, planes, blocks, stride, dilation):
    downsample = None
    if ((stride != 1) or (self.inplanes != (planes * block.expansion))):
        downsample = nn.Sequential(conv1x1(self.inplanes, (planes * block.expansion), stride), nn.BatchNorm2d((planes * block.expansion)))
    layers = []
    layers.append(block(self.inplanes, planes, stride, dilation, downsample))
    self.inplanes = (planes * block.expansion)
    for _ in range(1, blocks):
        layers.append(block(self.inplanes, planes, 1, dilation))
    return nn.Sequential(*layers)
def _make_layer(self, block, planes, blocks, stride, dilation):
    downsample = None
    if ((stride != 1) or (self.inplanes != (planes * block.expansion))):
        downsample = nn.Sequential(conv1x1(self.inplanes, (planes * block.expansion), stride), nn.BatchNorm2d((planes * block.expansion)))
    layers = []
    layers.append(block(self.inplanes, planes, stride, dilation, downsample))
    self.inplanes = (planes * block.expansion)
    for _ in range(1, blocks):
        layers.append(block(self.inplanes, planes, 1, dilation))
    return nn.Sequential(*layers)