def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
    """Creates a MixNet Small model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
    Paper: https://arxiv.org/abs/1907.09595
    """
    arch_def = [
        # stage 0, 112x112 in
        ['ds_r1_k3_s1_e1_c16'],  # relu
        # stage 1, 112x112 in
        ['ir_r1_k3_a1.1_p1.1_s2_e6_c24', 'ir_r1_k3_a1.1_p1.1_s1_e3_c24'],  # relu
        # stage 2, 56x56 in
        ['ir_r1_k3.5.7_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'],  # swish
        # stage 3, 28x28 in
        ['ir_r1_k3.5.7_p1.1_s2_e6_c80_se0.25_nsw', 'ir_r2_k3.5_p1.1_s1_e6_c80_se0.25_nsw'],  # swish
        # stage 4, 14x14in
        ['ir_r1_k3.5.7_a1.1_p1.1_s1_e6_c120_se0.5_nsw', 'ir_r2_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'],  # swish
        # stage 5, 14x14in
        ['ir_r1_k3.5.7.9.11_s2_e6_c200_se0.5_nsw', 'ir_r2_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'],  # swish
        # 7x7
    ]
    with layer_config_kwargs(kwargs):
        model_kwargs = dict(
            block_args=decode_arch_def(arch_def),
            num_features=1536,
            stem_size=16,
            channel_multiplier=channel_multiplier,
            act_layer=resolve_act_layer(kwargs, 'relu'),
            norm_kwargs=resolve_bn_args(kwargs),
            **kwargs
        )
        model = _create_model(model_kwargs, variant, pretrained)
    return model
Creates a MixNet Small model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet
    Paper: https://arxiv.org/abs/1907.09595
    
ControlNet-v1-1-nightly/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/gen_efficientnet.py