def METHOD_NAME(image_path):
    image_path = Path(image_path)
    image_raw = Image.open(image_path)
    image = image_raw.resize(
        (800, int(800 * image_raw.size[1] / image_raw.size[0])),
        Image.Resampling.LANCZOS)
    # prepare image for the model
    encoding = feature_extractor(image, return_tensors="pt")
    # forward pass
    with torch.no_grad():
        outputs = model(**encoding)
        predicted_depth = outputs.predicted_depth
    # interpolate to original size
    prediction = torch.nn.functional.interpolate(
        predicted_depth.unsqueeze(1),
        size=image.size[::-1],
        mode="bicubic",
        align_corners=False,
    ).squeeze()
    output = prediction.cpu().numpy()
    depth_image = (output * 255 / np.max(output)).astype('uint8')
    try:
        gltf_path = create_3d_obj(np.array(image), depth_image, image_path)
        img = Image.fromarray(depth_image)
        return [img, gltf_path, gltf_path]
    except Exception:
        gltf_path = create_3d_obj(
            np.array(image), depth_image, image_path, depth=8)
        img = Image.fromarray(depth_image)
        return [img, gltf_path, gltf_path]
    except:
        print("Error reconstructing 3D model")
        raise Exception("Error reconstructing 3D model")