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.
class_docstr_tok: ["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","."]
docstr: Predict probabilities on given data by the data indexes.
docstr_tok: ["Predict","probabilities","on","given","data","by","the","data","indexes","."]
code: def _predict_proba(
      self, data: TextData
  ) -> (
      TTextProba
  ):  # TODO: Needs to receive list of strings, not TextData
      """Predict probabilities on given data by the data indexes."""
      if self.validate_data_on_predict:
          data_indices = set(
              np.random.choice(
                  data.get_original_text_indexes(),
                  min(100, len(data)),
                  replace=False,
              )
          )
          if not data_indices.issubset(self._proba_indices[data.name]):
              raise DeepchecksValueError(
                  "Data that has not been seen before passed for inference with pre computed "
                  "probabilities."
              )
      return list(
          itemgetter(*data.get_original_text_indexes())(
              self.probas[data.name]
          )
      )
  
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 3 for calling the _DummyModel obj's underlying member method code with example usage: obj._predict_proba(data) and returns: list