def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
    """Creates an EfficientNet-Lite model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite
    Paper: https://arxiv.org/abs/1905.11946

    EfficientNet params
    name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
      'efficientnet-lite0': (1.0, 1.0, 224, 0.2),
      'efficientnet-lite1': (1.0, 1.1, 240, 0.2),
      'efficientnet-lite2': (1.1, 1.2, 260, 0.3),
      'efficientnet-lite3': (1.2, 1.4, 280, 0.3),
      'efficientnet-lite4': (1.4, 1.8, 300, 0.3),

    Args:
      channel_multiplier: multiplier to number of channels per layer
      depth_multiplier: multiplier to number of repeats per stage
    """
    arch_def = [
        ['ds_r1_k3_s1_e1_c16'],
        ['ir_r2_k3_s2_e6_c24'],
        ['ir_r2_k5_s2_e6_c40'],
        ['ir_r3_k3_s2_e6_c80'],
        ['ir_r3_k5_s1_e6_c112'],
        ['ir_r4_k5_s2_e6_c192'],
        ['ir_r1_k3_s1_e6_c320'],
    ]
    with layer_config_kwargs(kwargs):
        model_kwargs = dict(
            block_args=decode_arch_def(arch_def, depth_multiplier, fix_first_last=True),
            num_features=1280,
            stem_size=32,
            fix_stem=True,
            channel_multiplier=channel_multiplier,
            act_layer=nn.ReLU6,
            norm_kwargs=resolve_bn_args(kwargs),
            **kwargs,
        )
        model = _create_model(model_kwargs, variant, pretrained)
    return model
Creates an EfficientNet-Lite model.

    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite
    Paper: https://arxiv.org/abs/1905.11946

    EfficientNet params
    name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
      'efficientnet-lite0': (1.0, 1.0, 224, 0.2),
      'efficientnet-lite1': (1.0, 1.1, 240, 0.2),
      'efficientnet-lite2': (1.1, 1.2, 260, 0.3),
      'efficientnet-lite3': (1.2, 1.4, 280, 0.3),
      'efficientnet-lite4': (1.4, 1.8, 300, 0.3),

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