code: def _njit_softmin3(a, b, c, gamma):
      r"""Compute softmin of 3 input variables with parameter gamma.
  
      In the limit case :math:`\gamma = 0`, the softmin operator reduces to
      a hard-min operator.
  
      Parameters
      ----------
      a : float64
          First input variable.
      b : float64
          Second input variable.
      c : float64
          Third input variable.
      gamma : float64
          Regularization parameter.
  
      Returns
      -------
      softmin_value : float64
          Softmin value.
      """
      a /= -gamma
      b /= -gamma
      c /= -gamma
  
      max_val = max(max(a, b), c)
  
      tmp = 0
      tmp += np.exp(a - max_val)
      tmp += np.exp(b - max_val)
      tmp += np.exp(c - max_val)
      softmin_value = -gamma * (np.log(tmp) + max_val)
      return softmin_value
  
file_path: tslearn/tslearn/metrics/soft_dtw_fast.py
imports: import numpy
  from numba import njit, prange
  from tslearn.backend.backend import instantiate_backend
label_desc: Use image node_id 1 for calling a global function with example usage: _njit_softmin3(a, b, c, gamma) and returns: softmin_value