code: def train(self, mode=True):
      # If we're going in training mode, we need to clear any pre-comptued caches from eval mode
      if (self.training and not mode) or mode:
          self._clear_cache()
      return super().train(mode=mode)
  
imports: import copy
  import inspect
  import itertools
  import operator
  from collections import OrderedDict
  from typing import Union
  import torch
  from linear_operator.operators import LinearOperator
  from torch import nn, Tensor
  from torch.distributions import Distribution
  from .constraints import Interval
label_desc: Use image node_id 20 for calling the Module obj's underlying member method code with example usage: obj.train(mode) and returns: super