code: def _inverse_binarize_thresholding(
      y, output_type, classes, threshold
  ):
      """Inverse label binarization transformation using thresholding."""
  
      if output_type == "binary" and y.ndim == 2 and y.shape[1] > 2:
          raise ValueError(
              "output_type='binary', but y.shape = {0}".format(y.shape)
          )
  
      if output_type != "binary" and y.shape[1] != len(classes):
          raise ValueError(
              "The number of class is not equal to the number of dimension of y."
          )
  
      classes = np.asarray(classes)
  
      # Perform thresholding
      if sp.issparse(y):
          if threshold > 0:
              if y.format not in ("csr", "csc"):
                  y = y.tocsr()
              y.data = np.array(y.data > threshold, dtype=int)
              y.eliminate_zeros()
          else:
              y = np.array(y.toarray() > threshold, dtype=int)
      else:
          y = np.array(y > threshold, dtype=int)
  
      # Inverse transform data
      if output_type == "binary":
          if sp.issparse(y):
              y = y.toarray()
          if y.ndim == 2 and y.shape[1] == 2:
              return classes[y[:, 1]]
          else:
              if len(classes) == 1:
                  return np.repeat(classes[0], len(y))
              else:
                  return classes[y.ravel()]
  
      elif output_type == "multilabel-indicator":
          return y
  
      else:
          raise ValueError(
              "{0} format is not supported".format(output_type)
          )
  
imports: import array
  import itertools
  import warnings
  from collections import defaultdict
  from numbers import Integral
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, TransformerMixin, _fit_context
  from ..utils import column_or_1d
  from ..utils._encode import _encode, _unique
  from ..utils._param_validation import Interval, validate_params
  from ..utils.multiclass import type_of_target, unique_labels
  from ..utils.sparsefuncs import min_max_axis
  from ..utils.validation import _num_samples, check_array, check_is_fitted
label_desc: Use image node_id 3 for calling a global function with example usage: _inverse_binarize_thresholding(y, output_type, classes, threshold) and returns: classes, y, np, classes