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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def readImages(self, path, recursive=False, numPartitions=-1, dropImageFailures=False, sampleRatio=1.0, seed=0):
 """ 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. 5 .. versionadded:: 2.3.0 """

        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)