code: def _float_matching(X_col, return_safe_col=False):
      try:
          is_floaty = X_col.str.match(_FLOAT_REGEX)
      except TypeError as e:
          warn(f"Error in string parsing:{str(e)}")
          return pd.Series(False, index=X_col.index)
      # things that weren't strings
      not_strings = is_floaty.isna()
      if not_strings.any():
          rest = X_col[not_strings]
          all_castable = False
          try:
              # if we can convert them all to float we're done
              rest.astype(float)
              is_floaty[not_strings] = True
              all_castable = True
          except (ValueError, TypeError):
              pass
          if not all_castable:
              if X_col.name not in _MIXED_TYPE_WARNINGS:
                  warn(f"Mixed types in column {X_col.name}")
                  _MIXED_TYPE_WARNINGS[X_col.name] = True
              # make everything string
              rest = rest.astype(str)
              rest_is_floaty = _float_matching(rest)
              is_floaty[not_strings] = rest_is_floaty
              if return_safe_col:
                  X_col = X_col.copy()
                  X_col[not_strings] = rest
  
      if not is_floaty.dtype == bool:
          is_floaty = is_floaty.astype(bool)
  
      if return_safe_col:
          return is_floaty, X_col
      else:
          return is_floaty
  
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 1 for calling a global function with example usage: _float_matching(X_col, return_safe_col) and returns: is_floaty,  X_col, is_floaty, pd