code: def _cut_continuous(
      native, X_col, processing, binning, max_bins, min_samples_bin
  ):
      # called under: fit
  
      if (
          processing != "quantile"
          and processing != "rounded_quantile"
          and processing != "uniform"
          and processing != "winsorized"
          and not isinstance(processing, list)
          and not isinstance(processing, np.ndarray)
      ):
          if isinstance(binning, list) or isinstance(
              binning, np.ndarray
          ):
              msg = f"illegal binning type {binning}"
              _log.error(msg)
              raise ValueError(msg)
          processing = binning
  
      if processing == "quantile":
          # one bin for missing, one bin for unknown, and # of cuts is one less again
          cuts = native.cut_quantile(
              X_col, min_samples_bin, 0, max_bins - 3
          )
      elif processing == "rounded_quantile":
          # one bin for missing, one bin for unknown, and # of cuts is one less again
          cuts = native.cut_quantile(
              X_col, min_samples_bin, 1, max_bins - 3
          )
      elif processing == "uniform":
          # one bin for missing, one bin for unknown, and # of cuts is one less again
          cuts = native.cut_uniform(X_col, max_bins - 3)
      elif processing == "winsorized":
          # one bin for missing, one bin for unknown, and # of cuts is one less again
          cuts = native.cut_winsorized(X_col, max_bins - 3)
      elif isinstance(processing, np.ndarray):
          cuts = processing.astype(dtype=np.float64, copy=False)
      elif isinstance(processing, list):
          cuts = np.array(processing, np.float64)
      else:
          msg = f"illegal binning type {processing}"
          _log.error(msg)
          raise ValueError(msg)
  
      return cuts
  
imports: import logging
  import math
  from itertools import count, repeat, groupby
  from warnings import warn
  import numpy
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.validation import check_is_fitted
  from ._clean_x import unify_columns, preclean_X, unify_feature_names
  from ._clean_simple import clean_dimensions
  from ._seed import normalize_seed, increment_seed
  from ._native import Native
  from ._privacy import validate_eps_delta, calc_classic_noise_multi, calc_gdp_noise_multi, private_numeric_binning, private_categorical_binning
label_desc: Use image node_id 1 for calling a global function with example usage: _cut_continuous(native, X_col, processing, binning, max_bins, min_samples_bin) and returns: cuts