class_docstr: Encapsulate scorer function with extra methods.
  
  Scorer functions are functions used to compute various performance metrics, using the model and data as inputs,
  rather than the labels and predictions. Scorers are callables with the signature scorer(model, features, y_true).
  Additional data on scorer functions can be found at https://scikit-learn.org/stable/modules/model_evaluation.html.
  
  Parameters
  ----------
  scorer : t.Union[str, t.Callable]
      sklearn scorer name or callable
  model_classes: t.Optional[t.List]
      possible classes output for model. None for regression tasks.
  name : str, default = None
      scorer name
docstr: Return data of dataset without null labels.
docstr_tok: ["Return","data","of","dataset","without","null","labels","."]
code: def filter_nulls(
      cls, dataset: "tabular.Dataset"
  ) -> "tabular.Dataset":
      """Return data of dataset without null labels."""
      valid_idx = dataset.data[dataset.label_name].notna()
      return dataset.copy(dataset.data[valid_idx])
  
imports: import logging
  import typing
  import warnings
  from numbers import Number
  from typing import TYPE_CHECKING
  import numpy
  import pandas
  from packaging import version
  from sklearn import __version__
  from sklearn.base import ClassifierMixin
  from sklearn.metrics import get_scorer, log_loss, make_scorer, mean_absolute_error, mean_squared_error
  from sklearn.metrics._scorer import _BaseScorer, _ProbaScorer
  from sklearn.preprocessing import OneHotEncoder
  from deepchecks.core import errors
  from deepchecks.tabular.metric_utils.additional_classification_metrics import false_negative_rate_metric, false_positive_rate_metric, roc_auc_per_class, true_negative_rate_metric
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.docref import doclink
  from deepchecks.utils.logger import get_logger
  from deepchecks.utils.metrics import get_scorer_name
  from deepchecks.utils.simple_models import PerfectModel
  from deepchecks.utils.typing import BasicModel
  from deepchecks.utils.validation import is_sequence_not_str
label_desc: Use image node_id 2 for calling the DeepcheckScorer obj's underlying member method code with example usage: obj.filter_nulls(cls, dataset) and returns: dataset