src/transformers/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py/get_mobilenet_v2_config
def get_mobilenet_v2_config(model_name):
    config = MobileNetV2Config(layer_norm_eps=0.001)

    if "quant" in model_name:
        raise ValueError("Quantized models are not supported.")

    matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name)
    if matches:
        config.depth_multiplier = float(matches[1])
        config.image_size = int(matches[2])

    if model_name.startswith("deeplabv3_"):
        config.output_stride = 8
        config.num_labels = 21
        filename = "pascal-voc-id2label.json"
    else:
        # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
        # of the usual 1000. The first class (index 0) is "background".
        config.num_labels = 1001
        filename = "imagenet-1k-id2label.json"

    repo_id = "huggingface/label-files"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))

    if config.num_labels == 1001:
        id2label = {int(k) + 1: v for k, v in id2label.items()}
        id2label[0] = "background"
    else:
        id2label = {int(k): v for k, v in id2label.items()}

    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}

    return config