def METHOD_NAME(self, in_channels, channels, num_convs, use_conv_module):
    if use_conv_module:
        convs = [
            layers.ConvBNReLU(
                in_channels=in_channels,
                out_channels=channels,
                kernel_size=1,
                bias_attr=False)
        ]
        for _ in range(num_convs - 1):
            convs.append(
                layers.ConvBNReLU(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=1,
                    bias_attr=False))
    else:
        convs = [nn.Conv2D(in_channels, channels, 1)]
        for _ in range(num_convs - 1):
            convs.append(nn.Conv2D(channels, channels, 1))
    if len(convs) > 1:
        convs = nn.Sequential(*convs)
    else:
        convs = convs[0]
    return convs