code: def readImages(self, path, recursive=False, numPartitions=-1,
                     dropImageFailures=False, sampleRatio=1.0, seed=0):
          
          warnings.warn("`ImageSchema.readImage` is deprecated. " +
                        "Use `spark.read.format(\"image\").load(path)` instead.", DeprecationWarning)
          spark = SparkSession.builder.getOrCreate()
          image_schema = spark._jvm.org.apache.spark.ml.image.ImageSchema
          jsession = spark._jsparkSession
          jresult = image_schema.readImages(path, jsession, recursive, numPartitions,
                                            dropImageFailures, float(sampleRatio), seed)
          return DataFrame(jresult, spark._wrapped)
docs: Reads the directory of images from the local or remote source.
  
          .. note:: If multiple jobs are run in parallel with different sampleRatio or recursive flag,
              there may be a race condition where one job overwrites the hadoop configs of another.
  
          .. note:: If sample ratio is less than 1, sampling uses a PathFilter that is efficient but
              potentially non-deterministic.
  
          .. note:: Deprecated in 2.4.0. Use `spark.read.format("image").load(path)` instead and
              this `readImages` will be removed in 3.0.0.
  
          :param str path: Path to the image directory.
          :param bool recursive: Recursive search flag.
          :param int numPartitions: Number of DataFrame partitions.
          :param bool dropImageFailures: Drop the files that are not valid images.
          :param float sampleRatio: Fraction of the images loaded.
          :param int seed: Random number seed.
          :return: a :class:`DataFrame` with a single column of "images",
                 see ImageSchema for details.
  
          >>> df = ImageSchema.readImages('data/mllib/images/origin/kittens', recursive=True)
          >>> df.count()
          5
  
          .. versionadded:: 2.3.0