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: Override to calculate and log other metrics
docstr_tok: ["Override","to","calculate","and","log","other","metrics"]
code: def other_metrics(self, output: torch.Tensor, target: torch.Tensor):
      """Override to calculate and log other metrics"""
      pass
  
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 2 for calling the NLPAutoRegressionConfigs obj's underlying member method code with example usage: obj.other_metrics(output, target) without return types