code: def _get_field_converter(field_type: str) -> Callable[[Any], Any]:
      if field_type == "vector":
  
          def converter(value: Any) -> Any:
              if value["type"] == 1:
                  # dense vector
                  return value["values"]
              if value["type"] == 0:
                  # sparse vector
                  size = int(value["size"])
                  sparse_array = np.zeros(size, dtype=np.float64)
                  sparse_array[value["indices"]] = value["values"]
                  return sparse_array
  
      elif field_type in [
          "float",
          "double",
          "int",
          "bigint",
          "smallint",
          "array<float>",
          "array<double>",
          "array<int>",
          "array<bigint>",
          "array<smallint>",
      ]:
  
          def converter(value: Any) -> Any:
              return value
  
      else:
          raise ValueError(
              "SparkPartitionTorchDataset does not support loading data from field of "
              f"type {field_type}."
          )
      return converter
  
file_path: spark/python/pyspark/ml/torch/data.py
imports: from typing import Any, Callable, Iterator
  import torch
  import numpy
  from pyspark.sql.types import StructType
label_desc: Use image node_id 2 for calling the _SparkPartitionTorchDataset obj's underlying member method code with example usage: obj._get_field_converter(field_type) and returns: converter, value, sparse_array, value