def seresnext26d_32x4d(pretrained=False, **kwargs):
    """Constructs a SE-ResNeXt-26-D model.`
    This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for
    combination of deep stem and avg_pool in downsample.
    """
    model_args = dict(
        block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
        stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'), **kwargs)
    return _create_resnet('seresnext26d_32x4d', pretrained, **model_args)
Constructs a SE-ResNeXt-26-D model.`
    This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for
    combination of deep stem and avg_pool in downsample.
    
analyze/convnexts4nd/src/models/baselines/resnet_timm.py