code: def test_torch_primitive(spark_test_ctx):
      import torch
  
      schema = StructType(
          [
              StructField("bool_col", BooleanType(), False),
              StructField("float_col", FloatType(), False),
              StructField("double_col", DoubleType(), False),
              StructField("short_col", ShortType(), False),
              StructField("int_col", IntegerType(), False),
              StructField("long_col", LongType(), False),
              StructField("byte_col", ByteType(), False),
          ]
      )
      df = spark_test_ctx.spark.createDataFrame(
          [
              (True, 0.12, 432.1, 5, 5, 0, -128),
              (False, 123.45, 0.987, 9, 908, 765, 127),
          ],
          schema=schema,
      ).coalesce(1)
      # If we use numPartition > 1, the order of the loaded dataset would
      # be non-deterministic.
      expected_df = df.collect()
  
      converter = make_spark_converter(df)
      batch = None
      with converter.make_torch_dataloader(num_epochs=1) as dataloader:
          for i, batch in enumerate(dataloader):
              # default batch_size = 1
              for col in df.schema.names:
                  actual_ele = batch[col][0]
                  expected_ele = expected_df[i][col]
                  if col == "float_col" or col == "double_col":
                      # Note that the default dtype is float32
                      assert (
                          pytest.approx(expected_ele, rel=1e-6)
                          == actual_ele
                      )
                  else:
                      assert expected_ele == actual_ele
  
          assert len(expected_df) == len(converter)
      assert torch.uint8 == batch["bool_col"].dtype
      assert torch.int8 == batch["byte_col"].dtype
      assert torch.float32 == batch["double_col"].dtype
      assert torch.float32 == batch["float_col"].dtype
      assert torch.int32 == batch["int_col"].dtype
      assert torch.int64 == batch["long_col"].dtype
      assert torch.int16 == batch["short_col"].dtype
  
imports: import os
  import subprocess
  import sys
  import tempfile
  import threading
  import time
  from distutils.version import LooseVersion
  import numpy
  import pyspark
  import pytest
  import py4j
  import tensorflow.compat.v1
  from pyspark.sql.functions import pandas_udf
  from pyspark.sql.types import ArrayType, BinaryType, BooleanType, ByteType, DoubleType, FloatType, IntegerType, LongType, ShortType, StringType, StructField, StructType
  from six.moves.urllib.parse import urlparse
  from petastorm import make_batch_reader
  from petastorm.fs_utils import FilesystemResolver
  from petastorm.spark import SparkDatasetConverter, make_spark_converter, spark_dataset_converter
  from petastorm.spark.spark_dataset_converter import _check_dataset_file_median_size, _check_parent_cache_dir_url, _check_rank_and_size_consistent_with_horovod, _check_url, _get_horovod_rank_and_size, _get_spark_session, _make_sub_dir_url, register_delete_dir_handler, _wait_file_available, _normalize_databricks_dbfs_url
  from unittest import mock
  from petastorm.tests.test_tf_utils import create_tf_graph
label_desc: Use image node_id 19 for calling a global function with example usage: test_torch_primitive(spark_test_ctx) without return types