class_docstr: Aim Logger: logs metrics in Aim format.
  
  Aim is an open-source, self-hosted ML experiment tracking tool.
  It's good at tracking lots (thousands) of training runs, and it allows you to
  compare them with a performant and well-designed UI.
  
  Source: https://github.com/aimhubio/aim
  
  Args:
      repo: Aim repository directory or a `Repo` object that the Run object will
          log results to. If not provided, a default repo will be set up in the
          experiment directory (one level above trial directories).
      experiment: Sets the `experiment` property of each Run object, which is the
          experiment name associated with it. Can be used later to query
          runs/sequences.
          If not provided, the default will be the Tune experiment name set
          by `RunConfig(name=...)`.
      metrics: List of metric names (out of the metrics reported by Tune) to
          track in Aim. If no metric are specified, log everything that
          is reported.
      aim_run_kwargs: Additional arguments that will be passed when creating the
          individual `Run` objects for each trial. For the full list of arguments,
          please see the Aim documentation:
          https://aimstack.readthedocs.io/en/latest/refs/sdk.html
docstr: Initializes an Aim Run object for a given trial.
  
  Args:
      trial: The Tune trial that aim will track as a Run.
  
  Returns:
      Run: The created aim run for a specific trial.
docstr_tok: ["Initializes","an","Aim","Run","object","for","a","given","trial",".","Args",":","trial",":","The","Tune","trial","that","aim","will","track","as","a","Run",".","Returns",":","Run",":","The","created","aim","run","for","a","specific","trial","."]
code: def _create_run(self, trial: "Trial") -> Run:
      """Initializes an Aim Run object for a given trial.
  
      Args:
          trial: The Tune trial that aim will track as a Run.
  
      Returns:
          Run: The created aim run for a specific trial.
      """
      experiment_dir = trial.local_experiment_path
      run = Run(
          repo=self._repo_path or experiment_dir,
          experiment=self._experiment_name or trial.experiment_dir_name,
          **self._aim_run_kwargs,
      )
      # Attach a few useful trial properties
      run["trial_id"] = trial.trial_id
      run["trial_log_dir"] = trial.path
      trial_ip = trial.get_ray_actor_ip()
      if trial_ip:
          run["trial_ip"] = trial_ip
      return run
  
file_path: ray/python/ray/tune/logger/aim.py
imports: import logging
  import numpy
  from typing import TYPE_CHECKING, Dict, Optional, List, Union
  from ray.air.constants import TRAINING_ITERATION
  from ray.tune.logger.logger import LoggerCallback
  from ray.tune.result import TIME_TOTAL_S, TIMESTEPS_TOTAL
  from ray.tune.utils import flatten_dict
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 2 for calling the AimLoggerCallback obj's underlying member method code with example usage: obj._create_run(trial) and returns: run