code: def _load_metadata_from_fs(fs, path):
      with fs.open(path, "rb") as train_meta_file:
          meta = train_meta_file.read()
          meta = codec.loads_base64(meta.decode())
          data_schema = meta["schema"]
          rows = meta["rows"]
          total_byte_size = meta["total_byte_size"]
  
      return data_schema, rows, total_byte_size
  
imports: import horovod.spark.common._namedtuple_fix
  import contextlib
  import os
  import time
  from multiprocessing.pool import ThreadPool
  import pyarrow
  import numpy
  import pyspark.sql.functions
  from pyspark.ml.linalg import DenseVector, SparseVector, Vector, VectorUDT
  from pyspark.sql.types import ArrayType, BinaryType, BooleanType, FloatType, DoubleType, IntegerType, LongType, NullType, StringType
  from pyspark.sql import SparkSession
  from horovod.runner.common.util import codec, host_hash
  from horovod.spark.common import cache, constants
label_desc: Use image node_id 14 for calling a global function with example usage: _load_metadata_from_fs(fs, path) and returns: data_schema,  rows,  total_byte_size