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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 foreach(self, f):
 """ Sets the output of the streaming query to be processed using the provided writer ``f``. This is often used to write the output of a streaming query to arbitrary storage systems. The processing logic can be specified in two ways. #. A **function** that takes a row as input. This is a simple way to express your processing logic. Note that this does not allow you to deduplicate generated data when failures cause reprocessing of some input data. That would require you to specify the processing logic in the next way. #. An **object** with a ``process`` method and optional ``open`` and ``close`` methods. The object can have the following methods. * ``open(partition_id, epoch_id)``: *Optional* method that initializes the processing (for example, open a connection, start a transaction, etc). Additionally, you can use the `partition_id` and `epoch_id` to deduplicate regenerated data (discussed later). * ``process(row)``: *Non-optional* method that processes each :class:`Row`. * ``close(error)``: *Optional* method that finalizes and cleans up (for example, close connection, commit transaction, etc.) after all rows have been processed. The object will be used by Spark in the following way. * A single copy of this object is responsible of all the data generated by a single task in a query. In other words, one instance is responsible for processing one partition of the data generated in a distributed manner. * This object must be serializable because each task will get a fresh serialized-deserialized copy of the provided object. Hence, it is strongly recommended that any initialization for writing data (e.g. opening a method has been called, which signifies that the task is ready to generate data. * The lifecycle of the methods are as follows. For each partition with ``partition_id``: batch/epoch, method ``process(row)`` is called. processing rows. Important points to note: * The `partitionId` and `epochId` can be used to deduplicate generated data when failures cause reprocessing of some input data. This depends on the execution mode of the query. If the streaming query is being executed in the micro-batch mode, then every partition represented by a unique tuple (partition_id, epoch_id) is guaranteed to have the same data. Hence, (partition_id, epoch_id) can be used to deduplicate and/or transactionally commit data and achieve exactly-once guarantees. However, if the streaming query is being executed in the continuous mode, then this guarantee does not hold and therefore should not be used for deduplication. * The ``close()`` method (if exists) will be called if `open()` method exists and returns successfully (irrespective of the return value), except if the Python crashes in the middle. .. note:: Evolving. """


        from pyspark.rdd import _wrap_function
        from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
        from pyspark.taskcontext import TaskContext

        if callable(f):
            # The provided object is a callable function that is supposed to be called on each row.
            # Construct a function that takes an iterator and calls the provided function on each
            # row.
            def func_without_process(_, iterator):
                for x in iterator:
                    f(x)
                return iter([])

            func = func_without_process

        else:
            # The provided object is not a callable function. Then it is expected to have a
            # 'process(row)' method, and optional 'open(partition_id, epoch_id)' and
            # 'close(error)' methods.

            if not hasattr(f, 'process'):
                raise Exception("Provided object does not have a 'process' method")

            if not callable(getattr(f, 'process')):
                raise Exception("Attribute 'process' in provided object is not callable")

            def doesMethodExist(method_name):
                exists = hasattr(f, method_name)
                if exists and not callable(getattr(f, method_name)):
                    raise Exception(
                        "Attribute '%s' in provided object is not callable" % method_name)
                return exists

            open_exists = doesMethodExist('open')
            close_exists = doesMethodExist('close')

            def func_with_open_process_close(partition_id, iterator):
                epoch_id = TaskContext.get().getLocalProperty('streaming.sql.batchId')
                if epoch_id:
                    epoch_id = int(epoch_id)
                else:
                    raise Exception("Could not get batch id from TaskContext")

                # Check if the data should be processed
                should_process = True
                if open_exists:
                    should_process = f.open(partition_id, epoch_id)

                error = None

                try:
                    if should_process:
                        for x in iterator:
                            f.process(x)
                except Exception as ex:
                    error = ex
                finally:
                    if close_exists:
                        f.close(error)
                    if error:
                        raise error

                return iter([])

            func = func_with_open_process_close

        serializer = AutoBatchedSerializer(PickleSerializer())
        wrapped_func = _wrap_function(self._spark._sc, func, serializer, serializer)
        jForeachWriter = \
            self._spark._sc._jvm.org.apache.spark.sql.execution.python.PythonForeachWriter(
                wrapped_func, self._df._jdf.schema())
        self._jwrite.foreach(jForeachWriter)
        return self