code: def set_is_training(is_train):
      
      prev = ctypes.c_int()
      check_call(_LIB.MXAutogradSetIsTraining(
          ctypes.c_int(is_train), ctypes.byref(prev)))
      check_call(_LIB.MXAutogradSetIsRecording(
          ctypes.c_int(is_train), ctypes.byref(prev)))
      return bool(prev.value)
docs: Set status to training/not training. When training, graph will be constructed
      for gradient computation. Operators will also run with ctx.is_train=True. For example,
      Dropout will drop inputs randomly when is_train=True while simply passing through
      if is_train=False.
  
      Parameters
      ----------
      is_train: bool
  
      Returns
      -------
      previous state before this set.