code: def test_with_multiple_features(self):
      # Test that columns of ListArray, LargeListArray can be handled. Also test
      # that columns whose values are LargeBinaryArray can be handled.
      b1 = pa.RecordBatch.from_arrays(
          [
              pa.array(
                  [[1.0, 2.0], [3.0, 4.0, 5.0]],
                  type=pa.large_list(pa.float32()),
              ),
              pa.array(
                  [[b"x", b"y", b"z", b"w"], [b"qwe", b"abc"]],
                  type=pa.list_(pa.large_binary()),
              ),
              pa.array(
                  [
                      np.linspace(1, 1000, 1000, dtype=np.int32),
                      np.linspace(1001, 2000, 1000, dtype=np.int32),
                  ],
                  type=pa.list_(pa.int32()),
              ),
          ],
          ["a", "b", "c"],
      )
      b2 = pa.RecordBatch.from_arrays(
          [
              pa.array([[1.0]], type=pa.large_list(pa.float32())),
              pa.array([[b"ab"]], type=pa.list_(pa.large_binary())),
              pa.array(
                  [np.linspace(2001, 3000, 1000, dtype=np.int32)],
                  type=pa.list_(pa.int32()),
              ),
          ],
          ["a", "b", "c"],
      )
  
      batches = [b1, b2]
      expected_result = {
          types.FeaturePath(["a"]): text_format.Parse(
              """
              path {
                step: 'a'
              }
              type: FLOAT
              num_stats {
                common_stats {
                  num_non_missing: 3
                  min_num_values: 1
                  max_num_values: 3
                  avg_num_values: 2.0
                  tot_num_values: 6
                  num_values_histogram {
                    buckets {
                      low_value: 1.0
                      high_value: 2.0
                      sample_count: 2.0
                    }
                    buckets {
                      low_value: 2.0
                      high_value: 3.0
                      sample_count: 0.5
                    }
                    buckets {
                      low_value: 3.0
                      high_value: 3.0
                      sample_count: 0.5
                    }
                    type: QUANTILES
                  }
                }
                mean: 2.66666666
                std_dev: 1.49071198
                num_zeros: 0
                min: 1.0
                max: 5.0
                median: 3.0
                histograms {
                  buckets {
                    low_value: 1.0
                    high_value: 2.3333333
                    sample_count: 3.0049751
                  }
                  buckets {
                    low_value: 2.3333333
                    high_value: 3.6666667
                    sample_count: 1.0049751
                  }
                  buckets {
                    low_value: 3.6666667
                    high_value: 5.0
                    sample_count: 1.9900498
                  }
                  type: STANDARD
                }
                histograms {
                  buckets {
                    low_value: 1.0
                    high_value: 1.0
                    sample_count: 2.0
                  }
                  buckets {
                    low_value: 1.0
                    high_value: 3.0
                    sample_count: 2.0
                  }
                  buckets {
                    low_value: 3.0
                    high_value: 4.0
                    sample_count: 1.0
                  }
                  buckets {
                    low_value: 4.0
                    high_value: 5.0
                    sample_count: 1.0
                  }
                  type: QUANTILES
                }
              }
              """,
              statistics_pb2.FeatureNameStatistics(),
          ),
          types.FeaturePath(["b"]): text_format.Parse(
              """
              path {
                step: 'b'
              }
              type: STRING
              string_stats {
                common_stats {
                  num_non_missing: 3
                  min_num_values: 1
                  max_num_values: 4
                  avg_num_values: 2.33333333
                  tot_num_values: 7
                  num_values_histogram {
                    buckets {
                      low_value: 1.0
                      high_value: 2.0
                      sample_count: 2.0
                    }
                    buckets {
                      low_value: 2.0
                      high_value: 4.0
                      sample_count: 0.5
                    }
                    buckets {
                      low_value: 4.0
                      high_value: 4.0
                      sample_count: 0.5
                    }
                    type: QUANTILES
                  }
                }
                avg_length: 1.71428571
              }
              """,
              statistics_pb2.FeatureNameStatistics(),
          ),
          types.FeaturePath(["c"]): text_format.Parse(
              """
              path {
                step: 'c'
              }
              type: INT
              num_stats {
                common_stats {
                  num_non_missing: 3
                  min_num_values: 1000
                  max_num_values: 1000
                  avg_num_values: 1000.0
                  tot_num_values: 3000
                  num_values_histogram {
                    buckets {
                      low_value: 1000.0
                      high_value: 1000.0
                      sample_count: 1.0
                    }
                    buckets {
                      low_value: 1000.0
                      high_value: 1000.0
                      sample_count: 1.0
                    }
                    buckets {
                      low_value: 1000.0
                      high_value: 1000.0
                      sample_count: 1.0
                    }
                    type: QUANTILES
                  }
                }
                mean: 1500.5
                std_dev: 866.025355672
                min: 1.0
                max: 3000.0
                median: 1501.0
                histograms {
                  buckets {
                    low_value: 1.0
                    high_value: 1000.66666667
                    sample_count: 1000.6666667
                  }
                  buckets {
                    low_value: 1000.66666667
                    high_value: 2000.33333333
                    sample_count: 999.6666667
                  }
                  buckets {
                    low_value: 2000.33333333
                    high_value: 3000.0
                    sample_count: 999.6666667
                  }
                  type: STANDARD
                }
                histograms {
                  buckets {
                    low_value: 1.0
                    high_value: 751.0
                    sample_count: 751.0
                  }
                  buckets {
                    low_value: 751.0
                    high_value: 1501.0
                    sample_count: 750.0
                  }
                  buckets {
                    low_value: 1501.0
                    high_value: 2251.0
                    sample_count: 750.0
                  }
                  buckets {
                    low_value: 2251.0
                    high_value: 3000.0
                    sample_count: 749.0
                  }
                  type: QUANTILES
                }
              }
              """,
              statistics_pb2.FeatureNameStatistics(),
          ),
      }
      generator = basic_stats_generator.BasicStatsGenerator(
          num_values_histogram_buckets=3,
          num_histogram_buckets=3,
          num_quantiles_histogram_buckets=4,
          epsilon=0.001,
      )
      self.assertCombinerOutputEqual(
          batches, generator, expected_result
      )
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import pyarrow
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import basic_stats_generator
  from tensorflow_data_validation.utils import test_util
  from tensorflow_data_validation.utils.example_weight_map import ExampleWeightMap
  from google.protobuf import text_format
  from tensorflow_metadata.proto.v0 import schema_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 13 for calling the BasicStatsGeneratorTest obj's underlying member method code with example usage: obj.test_with_multiple_features() without return types