code: def _float_matching_fetch(X, col, return_safe_col=False):
      """Retrieve _float_matching for X[col] from cache or function call.
  
      If not present in cache, stores function call results into cache.
      Uses dataframe object id and column name as cache key.
      """
      hash_key = f"{col}-{hash(X[col])}"
  
      if hash_key in _FLOAT_MATCHING_CACHE:
          floats, X_col = _FLOAT_MATCHING_CACHE[hash_key]
      else:
          floats, X_col = _float_matching(X[col], return_safe_col=True)
          _FLOAT_MATCHING_CACHE[hash_key] = floats, X_col
  
      if return_safe_col:
          return floats, X_col
      else:
          return floats
  
imports: from importlib.metadata import version
  from joblib import hash
  import warnings
  from warnings import warn
  import numpy
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.compose import make_column_transformer, ColumnTransformer
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling a global function with example usage: _float_matching_fetch(X, col, return_safe_col) and returns: floats,  X_col, floats