code: def encode(self, entry: dict):
      result = {}
  
      for column in self.columns:
          value = entry[column]
  
          # We need to handle arrays with more than 1 dimension specially.
          # If we don't, pyarrow complains. As an optimisation, we flatten
          # the array to 1d and store its shape to gain zero-copy reads
          # during decoding.
          if column in self.ndarray_columns:
              if self.flatten_arrays:
                  result[f"{column}._np_shape"] = list(value.shape)
                  value = value.flatten()
              else:
                  value = list(value)
  
          result[column] = value
  
      return result
  
imports: from dataclasses import dataclass, field
  from functools import partial, singledispatch
  from itertools import chain
  from pathlib import Path
  from typing import List, Set, Optional, Union
  from typing_extensions import Literal
  from toolz.curried import keyfilter, valmap
  import numpy
  import pandas
  import pyarrow
  import pyarrow.parquet
  from gluonts.dataset import Dataset, DatasetWriter
  from gluonts.itertools import batcher, rows_to_columns
label_desc: Use image node_id 2 for calling the ArrowEncoder obj's underlying member method code with example usage: obj.encode(entry) and returns: result