def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
    """Creates a mnasnet-b1 model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
    Paper: https://arxiv.org/pdf/1807.11626.pdf.

    Args:
      channel_multiplier: multiplier to number of channels per layer.
    """
    arch_def = [
        ['ds_r1_k3_s1_c8'],
        ['ir_r1_k3_s2_e3_c16'],
        ['ir_r2_k3_s2_e6_c16'],
        ['ir_r4_k5_s2_e6_c32_se0.25'],
        ['ir_r3_k3_s1_e6_c32_se0.25'],
        ['ir_r3_k5_s2_e6_c88_se0.25'],
        ['ir_r1_k3_s1_e6_c144']
    ]
    with layer_config_kwargs(kwargs):
        model_kwargs = dict(
            block_args=decode_arch_def(arch_def),
            stem_size=8,
            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 mnasnet-b1 model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
    Paper: https://arxiv.org/pdf/1807.11626.pdf.

    Args:
      channel_multiplier: multiplier to number of channels per layer.
    
ControlNet-v1-1-nightly/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/gen_efficientnet.py