docstr: Reset the tensor with the shape provided or, otherwise, by
  using the one most recently provided. The `init_function`,
  if provided, does not override the default one.
  
  :param shape: the new shape or None to use the current one
  :param init_function: init function to use or None to use
      the default one.
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code: def reset_like(self, shape=None, init_function=None):
      """
      Reset the tensor with the shape provided or, otherwise, by
      using the one most recently provided. The `init_function`,
      if provided, does not override the default one.
  
      :param shape: the new shape or None to use the current one
      :param init_function: init function to use or None to use
          the default one.
      """
      if shape is not None:
          self.shape = torch.Size(shape)
      if init_function is None:
          init_function = self.init_function
      self._data = init_function(self.shape).to(self.device)
  
imports: from collections import defaultdict
  from typing import Callable, Dict, List, NamedTuple, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import Linear, Module
  from torch.utils.data import DataLoader, Dataset
  from avalanche.benchmarks import OnlineCLExperience
  from avalanche.models.batch_renorm import BatchRenorm2D
label_desc: Use image node_id 2 for calling the ParamData obj's underlying member method code with example usage: obj.reset_like(shape, init_function) without return types