code: def mion_time_derivative(tr, oversampling=16.0):
      """Implement the MION time derivative response function model.
  
      Parameters
      ----------
      tr: float
          scan repeat time, in seconds
      oversampling: int, optional
          temporal oversampling factor, optional
  
      Returns
      -------
      drf: array of shape(time_length / tr * oversampling, dtype=float)
          derived_response_function sampling on the provided grid
      """
      do = 0.1
      drf = (
          mion_response_function(tr, oversampling)
          - mion_response_function(tr, oversampling, do)
      ) / do
  
      return drf
  
imports: import numpy
  from scipy.stats import gamma
  import matplotlib.pyplot
  from nilearn.glm.first_level import compute_regressor
label_desc: Use image node_id 2 for calling a global function with example usage: mion_time_derivative(tr, oversampling) and returns: drf