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