def METHOD_NAME(self, block, planes, blocks, stride=1):
    """_make_layer.
    :param block: torch.nn.Module, class of blocks.
    :param planes: int,  number of channels produced by the convolution.
    :param blocks: int, number of layers in a block.
    :param stride: int, size of the convolving kernel.
    """
    downsample = None
    if stride != 1 or self.inplanes != planes * block.expansion:
        downsample = self.downsample_block(
            inplanes=self.inplanes,
            outplanes=planes * block.expansion,
            stride=stride,
        )
    layers = []
    layers.append(
        block(
            self.inplanes,
            planes,
            stride,
            downsample,
            relu_type=self.relu_type,
        )
    )
    self.inplanes = planes * block.expansion
    for _ in range(1, blocks):
        layers.append(
            block(
                self.inplanes,
                planes,
                relu_type=self.relu_type,
            )
        )
    return nn.Sequential(*layers)