code: def fit(self, X, y):
      # Check that X and y have correct shape
      # X, y = check_X_y(X, y)  # Commented out to allow for object dtypes
  
      # Store the classes seen during fit
      self.n_features_in_ = X.shape[1]
      self.classes_ = unique_labels(y)
  
      if len(self.classes_) == 1:
          raise ValueError(
              "Classifier can't train when only one class is present."
          )
      if len(self.classes_) == 2:
          problem_type = "binary"
      else:
          problem_type = "multiclass"
  
      init_args = self._get_init_args(problem_type=problem_type)
      fit_args = self._get_fit_args()
  
      self.predictor_ = TabularPredictor(**init_args)
  
      train_data = self._combine_X_y(X=X, y=y)
  
      self.predictor_.fit(train_data, **fit_args)
  
      # Return the classifier
      return self
  
file_path: autogluon/tabular/src/autogluon/tabular/experimental/_tabular_classifier.py
imports: from __future__ import annotations
  from typing import List
  import pandas
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils.multiclass import unique_labels
  from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
  from autogluon.core.metrics import Scorer
  from ..None import TabularPredictor
  from ._scikit_mixin import ScikitMixin
label_desc: Use image node_id 2 for calling the TabularClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: self