code: def test_predicate_on_single_column(synthetic_dataset):
      reader = make_reader(
          synthetic_dataset.url,
          schema_fields=[TestSchema.id2],
          predicate=in_lambda(["id2"], lambda id2: True),
          reader_pool_type="dummy",
      )
      counter = 0
      for row in reader:
          counter += 1
          actual = dict(row._asdict())
          assert actual["id2"] < 2
      assert counter == len(synthetic_dataset.data)
  
imports: import pytest
  import numpy
  from pyspark.sql import SparkSession
  from pyspark.sql.types import IntegerType
  from petastorm import make_reader
  from petastorm.codecs import ScalarCodec
  from petastorm.etl.dataset_metadata import materialize_dataset
  from petastorm.predicates import in_set, in_intersection, in_negate, in_reduce, in_pseudorandom_split, in_lambda
  from petastorm.tests.test_common import TestSchema
  from petastorm.unischema import dict_to_spark_row, Unischema, UnischemaField
label_desc: Use image node_id 11 for calling a global function with example usage: test_predicate_on_single_column(synthetic_dataset) without return types