code: def softmin(
      s: np.ndarray,
      *,
      temperature: float = 0.1,
      axis: int = 1,
      **_,
  ) -> np.ndarray:
      """Softmin score aggregation function.
  
      Parameters
      ----------
      s :
          Input array.
  
      temperature :
          Temperature parameter. Too small values may cause numerical underflow and NaN scores.
  
      axis :
          Axis along which to apply the function.
  
      Returns
      -------
          Softmin score.
      """
  
      return np.einsum(
          "ij,ij->i",
          s,
          softmax(
              x=1 - s, temperature=temperature, axis=axis, shift=True
          ),
      )
  
imports: from enum import Enum
  from typing import Callable, Dict, Optional, Union
  import numpy
  from sklearn.model_selection import cross_val_predict
  from cleanlab.internal.multilabel_utils import _is_multilabel, stack_complement
  from cleanlab.internal.label_quality_utils import _subtract_confident_thresholds
  from cleanlab.internal.numerics import softmax
  from cleanlab.rank import get_self_confidence_for_each_label, get_normalized_margin_for_each_label, get_confidence_weighted_entropy_for_each_label
label_desc: Use image node_id 2 for calling a global function with example usage: softmin(s) and returns: np