class_docstr: ## Configurations
  
  The default configurations can and will be overridden when we start the experiment.
class_docstr_tok: ["#","#","Configurations","The","default","configurations","can","and","will","be","overridden","when","we","start","the","experiment","."]
code: def init(self):
      # Set tracker configurations
      tracker.set_scalar("accuracy.*", True)
      tracker.set_scalar("loss.*", True)
      # Do not print the attention reconstruction loss in the terminal
      tracker.set_scalar("ar_loss.*", False)
      # Add a hook to log module outputs
      hook_model_outputs(self.mode, self.model, "model")
      # This will keep the accuracy metric stats and memories separate for training and validation.
      self.state_modules = [self.accuracy, self.memory]
  
imports: from typing import List, Tuple, NamedTuple
  import torch
  import torch.nn
  from labml import experiment, tracker, monit, logger
  from labml.configs import option
  from labml.logger import Text
  from labml_helpers.metrics.simple_state import SimpleStateModule
  from labml_helpers.module import Module
  from labml_helpers.train_valid import BatchIndex, hook_model_outputs
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.transformers.compressive import CompressiveTransformer, AttentionReconstructionLoss, CompressiveTransformerLayer, Conv1dCompression
label_desc: Use image node_id 1 for calling the Configs obj's underlying member method code with example usage: obj.init() without return types