class_docstr: Dummy model class used for inference with static predictions from the user.
  
  Parameters
  ----------
  train: TextData
      Dataset, representing data an estimator was fitted on.
  test: TextData
      Dataset, representing data an estimator predicts on.
  y_pred_train: np.ndarray
      Array of the model prediction over the train dataset.
  y_pred_test: 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.
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docstr: Just for python 3.6 (sklearn validates fit method).
docstr_tok: ["Just","for","python","3.6","(","sklearn","validates","fit","method",")","."]
code: def fit(self, *args, **kwargs):
      """Just for python 3.6 (sklearn validates fit method)."""
      pass
  
imports: import typing
  from operator import itemgetter
  import numpy
  from deepchecks.core.context import BaseContext
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.nlp.input_validations import _validate_multilabel, _validate_text_classification, _validate_token_classification, compare_dataframes
  from deepchecks.nlp.metric_utils.scorers import init_validate_scorers
  from deepchecks.nlp.metric_utils.token_classification import get_default_token_scorers, get_scorer_dict, validate_scorers
  from deepchecks.nlp.task_type import TaskType
  from deepchecks.nlp.text_data import TextData
  from deepchecks.nlp.utils.data_inference import infer_observed_and_model_labels
  from deepchecks.tabular.metric_utils import DeepcheckScorer, get_default_scorers
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.docref import doclink
  from deepchecks.utils.logger import get_logger
  from deepchecks.utils.typing import BasicModel
  from deepchecks.utils.validation import is_sequence_not_str
label_desc: Use image node_id 4 for calling the _DummyModel obj's underlying member method code with example usage: obj.fit() without return types