code: def _read_as_dict_rdd(path):
      sc = SparkContext.getOrCreate()
      spark = SparkSession(sc)
  
      df = spark.read.parquet(path)
      schema_path = os.path.join(path, "_orca_metadata")
  
      j_str = open_text(schema_path)[0]
  
      schema = decode_schema(j_str)
  
      rdd = df.rdd.map(lambda r: row_to_dict(schema, r))
      return rdd, schema
  
imports: from pyspark import SparkContext
  from pyspark.sql import SparkSession
  from zoo import init_nncontext
  from zoo.orca.data import SparkXShards
  from zoo.orca.data.file import open_text, write_text
  from zoo.orca.data.image.utils import chunks, dict_to_row, row_to_dict, encode_schema, decode_schema, SchemaField, FeatureType, DType, ndarray_dtype_to_dtype, decode_feature_type_ndarray, pa_fs
  from zoo.orca.data.image.voc_dataset import VOCDatasets
  from bigdl.util.common import get_node_and_core_number
  import os
  import numpy
  import random
  import pyarrow.parquet
  import io
  import math
label_desc: Use image node_id 2 for calling the ParquetDataset obj's underlying member method code with example usage: obj._read_as_dict_rdd(path) and returns: rdd,  schema