class_docstr: Watch via hooks the content of the model's activations during training
  computes basic statistics on them, and stream them to an 'observer'.
  
  This implementation only traces modules which:
  - do not have child modules (ex: nn.Sequential modules are ignored)
  - are in training mode (the goal is to identify divergences)
  
  Depending on the 'observer' implementation, the results can be
  accumulated or streamed to tensorboard (or any visualisation tool).
  
  Args:
      observer (ActivationStatisticsObserver):
          the observer to be notified upon each new trace
      log_frequency (int):
          frequency at which the monitoring of activations is done (ex: 1 is every step)
      ignored_modules (Set[str]):
          set of modules for which tracing the activation is disabled
      sample_feature_map (bool):
          If 'true' feature maps will only contribute one feature to the statistics
          (allows to reduce the compute overhead, while still maintaining the
          ability to detect divergence since in convolution nets, all weights
          are typically used for central elements of the feature map)
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docstr: Stop any form of tracking (removes the hooks used to monitor the model)
docstr_tok: ["Stop","any","form","of","tracking","(","removes","the","hooks","used","to","monitor","the","model",")"]
code: def stop(self):
      """
      Stop any form of tracking (removes the hooks used to monitor the model)
      """
      for h in self._hooks:
          h.remove()
      self._hooks.clear()
      self.iteration = -1
      self._previous_module_name = None
  
imports: import abc
  from typing import NamedTuple, Set
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 4 for calling the ActivationStatisticsMonitor obj's underlying member method code with example usage: obj.stop() without return types