code: def load(self, path=None, format=None, schema=None, **options):
          
          if format is not None:
              self.format(format)
          if schema is not None:
              self.schema(schema)
          self.options(**options)
          if path is not None:
              if type(path) != str or len(path.strip()) == 0:
                  raise ValueError("If the path is provided for stream, it needs to be a " +
                                   "non-empty string. List of paths are not supported.")
              return self._df(self._jreader.load(path))
          else:
              return self._df(self._jreader.load())
docs: Loads a data stream from a data source and returns it as a :class`DataFrame`.
  
          .. note:: Evolving.
  
          :param path: optional string for file-system backed data sources.
          :param format: optional string for format of the data source. Default to 'parquet'.
          :param schema: optional :class:`pyspark.sql.types.StructType` for the input schema
                         or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).
          :param options: all other string options
  
          >>> json_sdf = spark.readStream.format("json") \\
          ...     .schema(sdf_schema) \\
          ...     .load(tempfile.mkdtemp())
          >>> json_sdf.isStreaming
          True
          >>> json_sdf.schema == sdf_schema
          True