code: def _predict_proba(self, X: pd.DataFrame, **kwargs) -> np.ndarray:
      X = self.preprocess(X, **kwargs)
  
      pred_labels, pred_probs = self.model.predict(
          X, k=len(self.model.labels)
      )
  
      recs = []
      for labels, probs in zip(pred_labels, pred_probs):
          recs.append(
              dict(
                  zip(
                      (self._label_inv_map[label] for label in labels),
                      probs,
                  )
              )
          )
  
      y_pred_proba: np.ndarray = (
          pd.DataFrame(recs).sort_index(axis=1).values
      )
      return self._convert_proba_to_unified_form(y_pred_proba)
  
imports: import contextlib
  import gc
  import logging
  import os
  import tempfile
  import numpy
  import pandas
  from autogluon.common.features.types import S_TEXT
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.common.utils.try_import import try_import_fasttext
  from autogluon.core.constants import BINARY, MULTICLASS
  from autogluon.core.models import AbstractModel
  from .hyperparameters.parameters import get_param_baseline
label_desc: Use image node_id 8 for calling the FastTextModel obj's underlying member method code with example usage: obj._predict_proba(X) and returns: self