class_docstr: Dummy model class used for inference with static predictions from the user.
  
  Parameters
  ----------
  train: Dataset
      Dataset, representing data an estimator was fitted on.
  test: Dataset
      Dataset, representing data an estimator predicts on.
  y_pred_train: t.Optional[np.ndarray]
      Array of the model prediction over the train dataset.
  y_pred_test: t.Optional[np.ndarray]
      Array of the model prediction over the test dataset.
  y_proba_train: np.ndarray
      Array of the model prediction probabilities over the train dataset.
  y_proba_test: np.ndarray
      Array of the model prediction probabilities over the test dataset.
  validate_data_on_predict: bool, default = True
      If true, before predicting validates that the received data samples have the same index as in original data.
code: def _validate_data(self, data: pd.DataFrame):
      data = data.sample(min(100, len(data)))
      for feature_df in self.feature_df_list:
          # If all indices are found than test for equality in actual data (statistically significant portion)
          if set(data.index).issubset(set(feature_df.index)):
              sample_data = np.unique(np.random.choice(data.index, 30))
              if feature_df.loc[sample_data].equals(
                  data.loc[sample_data]
              ):
                  return
              else:
                  break
      raise DeepchecksValueError(
          "Data that has not been seen before passed for inference with static "
          "predictions. Pass a real model to resolve this"
      )
  
imports: import typing
  import numpy
  import pandas
  from deepchecks.core.context import BaseContext
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular._shared_docs import docstrings
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.metric_utils import DeepcheckScorer, get_default_scorers, init_validate_scorers
  from deepchecks.tabular.metric_utils.scorers import validate_proba
  from deepchecks.tabular.utils.feature_importance import calculate_feature_importance_or_none, validate_feature_importance
  from deepchecks.tabular.utils.task_inference import get_all_labels, infer_classes_from_model, infer_task_type_by_class_number, infer_task_type_by_labels
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.tabular.utils.validation import ensure_predictions_proba, ensure_predictions_shape, model_type_validation, validate_model
  from deepchecks.utils.docref import doclink
  from deepchecks.utils.logger import get_logger
  from deepchecks.utils.plot import DEFAULT_DATASET_NAMES
  from deepchecks.utils.typing import BasicModel
label_desc: Use image node_id 2 for calling the _DummyModel obj's underlying member method code with example usage: obj._validate_data(data) without return types