class_docstr: Find new labels in test.
  
  Parameters
  ----------
  n_samples : int , default: 10_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
class_docstr_tok: ["Find","new","labels","in","test",".","Parameters","--","--","--","--","--","n_samples",":","int",",","default",":","10_000_000","number","of","samples","to","use","for","this","check",".","random_state",":","int",",","default",":","42","random","seed","for","all","check","internals","."]
docstr: Reduce check result value.
  
  Returns
  -------
  Dict[str, float]
      number of samples per each new label
docstr_tok: ["Reduce","check","result","value",".","Returns","--","--","--","-","Dict","[","str",",","float","]","number","of","samples","per","each","new","label"]
code: def reduce_output(
      self, check_result: CheckResult
  ) -> Dict[str, float]:
      """Reduce check result value.
  
      Returns
      -------
      Dict[str, float]
          number of samples per each new label
      """
      if check_result.value["n_samples_per_new_label"]:
          value = sum(
              check_result.value["n_samples_per_new_label"].values()
          )
      else:
          value = 0
      return {"Samples with New Labels": value}
  
file_path: deepchecks/deepchecks/tabular/checks/train_test_validation/new_label_train_test.py
imports: from typing import Dict
  import pandas
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.core.reduce_classes import ReduceLabelMixin
  from deepchecks.tabular import Context, TrainTestCheck
  from deepchecks.utils.strings import format_percent
label_desc: Use image node_id 3 for calling the NewLabelTrainTest obj's underlying member method code with example usage: obj.reduce_output(check_result) and returns: dict