class_docstr: <a id="NLPAutoRegressionConfigs"></a>
  
  ## Trainer configurations
  
  This has the basic configurations for NLP auto-regressive task training.
  All the properties are configurable.
class_docstr_tok: ["<","a","id=","''","NLPAutoRegressionConfigs","''",">","<","\/a",">","#","#","Trainer","configurations","This","has","the","basic","configurations","for","NLP","auto-regressive","task","training",".","All","the","properties","are","configurable","."]
docstr: ### Initialization
docstr_tok: ["#","#","#","Initialization"]
code: def init(self):
      """
      ### Initialization
      """
      # Set tracker configurations
      tracker.set_scalar("accuracy.*", True)
      tracker.set_scalar("loss.*", True)
      tracker.set_text("sampled", False)
      # Add a hook to log module outputs
      hook_model_outputs(self.mode, self.model, "model")
      # Add accuracy as a state module.
      # The name is probably confusing, since it's meant to store
      # states between training and validation for RNNs.
      # This will keep the accuracy metric stats separate for training and validation.
      self.state_modules = [self.accuracy]
  
imports: from typing import Callable
  import torch
  import torch.nn
  from torch.utils.data import DataLoader, RandomSampler
  from labml import lab, monit, logger, tracker
  from labml.configs import option
  from labml.logger import Text
  from labml_helpers.datasets.text import TextDataset, SequentialDataLoader, SequentialUnBatchedDataset, TextFileDataset
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.metrics.accuracy import Accuracy
  from labml_helpers.module import Module
  from labml_helpers.train_valid import TrainValidConfigs, hook_model_outputs, BatchIndex
  from labml_nn.optimizers.configs import OptimizerConfigs
label_desc: Use image node_id 1 for calling the NLPAutoRegressionConfigs obj's underlying member method code with example usage: obj.init() without return types