class_docstr: Custom pipeline stage for handling dense feature arrays.
  
  A custom pipeline stage that will be inserted into the
  learner pipeline attribute to accommodate the situation
  when SKLL needs to manually convert feature arrays from
  sparse to dense. For example, when features are being hashed
  but we are also doing centering using the feature means.
docstr: Fit the estimator and transform the input.
docstr_tok: ["Fit","the","estimator","and","transform","the","input","."]
code: def fit_transform(self, X, y=None):
      """Fit the estimator and transform the input."""
      return self
  
imports: from __future__ import annotations
  import inspect
  import logging
  import sys
  import time
  from collections import Counter, defaultdict
  from csv import DictWriter, excel_tab
  from functools import wraps
  from importlib import import_module
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
  import joblib
  import numpy
  import scipy.sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest
  from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_fscore_support
  from sklearn.model_selection import KFold, LeaveOneGroupOut, ShuffleSplit, StratifiedKFold
  from skll.data import FeatureSet
  from skll.metrics import _CUSTOM_METRICS, _PREDEFINED_CUSTOM_METRICS, use_score_func
  from skll.types import ComputeEvalMetricsResults, ConfusionMatrix, FeaturesetIterator, FoldMapping, IdType, IndexIterator, LabelType, PathOrStr, SparseFeatureMatrix
  from skll.utils.constants import CLASSIFICATION_ONLY_METRICS, CORRELATION_METRICS, REGRESSION_ONLY_METRICS, UNWEIGHTED_KAPPA_METRICS, WEIGHTED_KAPPA_METRICS
  from skll.version import VERSION
label_desc: Use image node_id 2 for calling the Densifier obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self