code: def test_arrow_schema_convertion_fail():
      arrow_schema = pa.schema(
          [
              pa.field("list_of_int", pa.float16()),
          ]
      )
  
      mock_dataset = _mock_parquet_dataset([], arrow_schema)
  
      with pytest.raises(
          ValueError,
          match="Cannot auto-create unischema due to unsupported column type",
      ):
          Unischema.from_arrow_schema(
              mock_dataset, omit_unsupported_fields=False
          )
  
imports: from __future__ import division
  from decimal import Decimal
  import numpy
  import pyarrow
  import pyarrow.parquet
  import pytest
  from pyspark import Row
  from pyspark.sql.types import StringType, IntegerType, DecimalType, ShortType, LongType
  from petastorm.codecs import ScalarCodec, NdarrayCodec
  from petastorm.unischema import Unischema, UnischemaField, dict_to_spark_row, insert_explicit_nulls, match_unischema_fields, _new_gt_255_compatible_namedtuple, _fullmatch
  from unittest import mock
label_desc: Use image node_id 26 for calling a global function with example usage: test_arrow_schema_convertion_fail() without return types