def METHOD_NAME(img):
    """Resize image and make it fit for network.
    Args:
        img (array): image
    Returns:
        tensor: data ready for network
    """
    height_orig = img.shape[0]
    width_orig = img.shape[1]
    if width_orig > height_orig:
        scale = width_orig / 384
    else:
        scale = height_orig / 384
    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
    img_resized = (
        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
    )
    img_resized = img_resized.unsqueeze(0)
    return img_resized