code: def test_decode_numpy_scalar_when_codec_is_none():
      """Decoding a row that has a field with the codec set to None. The type should be deduced automatically
      from UnischemaField's numpy_dtype attribute"""
  
      MatrixSchema = Unischema(
          "TestSchema", [UnischemaField("scalar", np.float64, ())]
      )
      row = {"scalar": 42.0}
      decoded_value = decode_row(row, MatrixSchema)["scalar"]
      assert decoded_value == 42
      assert isinstance(decoded_value, np.float64)
  
file_path: petastorm/petastorm/tests/test_decode_row.py
imports: from decimal import Decimal
  import numpy
  import pytest
  from pyspark.sql.types import DoubleType
  from petastorm.codecs import NdarrayCodec, ScalarCodec
  from petastorm.unischema import UnischemaField, Unischema
  from petastorm.utils import decode_row, DecodeFieldError
label_desc: Use image node_id 3 for calling a global function with example usage: test_decode_numpy_scalar_when_codec_is_none() without return types