class_docstr: Log objects to Weights and Biases.
code: def log_model(self, model_file=None):
      if self.model_file is not None:
          if not model_file:
              model_file = self.model_file
          model_file = pathlib.Path(model_file)
          if model_file.exists():
              self.run.log_artifact(
                  str(model_file),
                  name=f"{self.run.name}-{model_file.name}",
                  type="model",
              )
          vocab_file = model_file.with_suffix(".dict")
          if vocab_file.exists():
              self.run.log_artifact(
                  str(vocab_file),
                  name=f"{self.run.name}-{vocab_file.name}",
                  type="vocab",
              )
          stats_file = model_file.with_suffix(".trainstats")
          if stats_file.exists():
              self.run.log_artifact(
                  str(stats_file),
                  name=f"{self.run.name}-{stats_file.name}",
                  type="stats",
              )
  
imports: import datetime
  import json
  import numbers
  import os
  import pathlib
  import re
  from typing import Optional
  import parlai.utils.logging
  from parlai.core.metrics import Metric, dict_report, get_metric_display_data
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.utils.io import PathManager
label_desc: Use image node_id 5 for calling the WandbLogger obj's underlying member method code with example usage: obj.log_model(model_file) without return types