docstr: Gather config parameters accessible to the learner
docstr_tok: ["Gather","config","parameters","accessible","to","the","learner"]
code: def gather_args(self):
      "Gather config parameters accessible to the learner"
      # args stored by 'store_attr'
      cb_args = {
          f"{cb}": getattr(cb, "__stored_args__", True)
          for cb in self.cbs
          if cb != self
      }
      args = {"Learner": self.learn, **cb_args}
      # input dim
      try:
          n_inp = self.dls.train.n_inp
          args["n_inp"] = n_inp
          xb = self.dls.valid.one_batch()[:n_inp]
          args.update(
              {
                  f"input {n+1} dim {i+1}": d
                  for n in range(n_inp)
                  for i, d in enumerate(list(detuplify(xb[n]).shape))
              }
          )
      except Exception:
          logger.warning("Failed to gather input dimensions")
      # other args
      with ignore_exceptions():
          args["batch_size"] = self.dls.bs
          args["batch_per_epoch"] = len(self.dls.train)
          args["model_parameters"] = total_params(self.model)[0]
          args["device"] = self.dls.device.type
          args["frozen"] = bool(self.opt.frozen_idx)
          args["frozen_idx"] = self.opt.frozen_idx
          args["dataset", "tfms"] = f"{self.dls.dataset.tfms}"
          args["dls", "after_item"] = f"{self.dls.after_item}"
          args["dls", "before_batch"] = f"{self.dls.before_batch}"
          args["dls", "after_batch"] = f"{self.dls.after_batch}"
      return args
  
file_path: aim/aim/sdk/adapters/fastai.py
imports: from logging import getLogger
  from typing import Optional
  from aim.sdk.run import Run
  from aim.ext.resource.configs import DEFAULT_SYSTEM_TRACKING_INT
label_desc: Use image node_id 9 for calling the AimCallback obj's underlying member method code with example usage: obj.gather_args() and returns: args