code: def test_peer_count(self, dtype_and_missing, use_mask):
      class C(Classifier):
          dtype = dtype_and_missing[0]
          missing_value = dtype_and_missing[1]
          inputs = ()
          window_length = 0
  
      c = C()
  
      if dtype_and_missing[0] == int64_dtype:
          data = np.array(
              [
                  [1, 1, -1, 2, 1, -1],
                  [2, 1, 3, 2, 2, 2],
                  [-1, 1, 10, 10, 10, -1],
                  [3, 3, 3, 3, 3, 3],
              ],
              dtype=int64_dtype,
          )
      else:
          data = LabelArray(
              [
                  ["a", "a", None, "b", "a", None],
                  ["b", "a", "c", "b", "b", "b"],
                  [None, "a", "aa", "aa", "aa", None],
                  ["c", "c", "c", "c", "c", "c"],
              ],
              missing_value=None,
          )
  
      if not use_mask:
          mask = self.build_mask(self.ones_mask(shape=data.shape))
          expected = np.array(
              [
                  [3, 3, np.nan, 1, 3, np.nan],
                  [4, 1, 1, 4, 4, 4],
                  [np.nan, 1, 3, 3, 3, np.nan],
                  [6, 6, 6, 6, 6, 6],
              ],
          )
      else:
          # Punch a couple holes in the mask to check that we handle the mask
          # correctly.
          mask = self.build_mask(
              np.array(
                  [
                      [1, 1, 1, 1, 0, 1],
                      [1, 1, 1, 1, 1, 0],
                      [1, 1, 1, 1, 1, 1],
                      [1, 1, 0, 0, 1, 1],
                  ],
                  dtype="bool",
              )
          )
          expected = np.array(
              [
                  [2, 2, np.nan, 1, np.nan, np.nan],
                  [3, 1, 1, 3, 3, np.nan],
                  [np.nan, 1, 3, 3, 3, np.nan],
                  [4, 4, np.nan, np.nan, 4, 4],
              ],
          )
  
      terms = {
          "peer_counts": c.peer_count(),
      }
      expected_results = {
          "peer_counts": expected,
      }
  
      self.check_terms(
          terms=terms,
          expected=expected_results,
          initial_workspace={c: data},
          mask=mask,
      )
  
imports: from functools import reduce
  import operator
  import numpy
  import pandas
  from zipline.lib.labelarray import LabelArray
  from zipline.pipeline import Classifier
  from zipline.pipeline.data.testing import TestingDataSet
  from zipline.pipeline.expression import methods_to_ops
  from zipline.testing import parameter_space
  from zipline.testing.fixtures import ZiplineTestCase
  from zipline.testing.predicates import assert_equal
  from zipline.utils.numpy_utils import categorical_dtype, int64_dtype
  from .base import BaseUSEquityPipelineTestCase
label_desc: Use image node_id 17 for calling the ClassifierTestCase obj's underlying member method code with example usage: obj.test_peer_count(dtype_and_missing, use_mask) without return types