class_docstr: Log sacred experiment data to W&B.
  
  Arguments:
      Accepts all the arguments accepted by wandb.init().
  
      name — A display name for this run, which shows up in the UI and is editable, doesn't have to be unique
      notes — A multiline string description associated with the run
      config — a dictionary-like object to set as initial config
      project — the name of the project to which this run will belong
      tags — a list of strings to associate with this run as tags
      dir — the path to a directory where artifacts will be written (default: ./wandb)
      entity — the team posting this run (default: your username or your default team)
      job_type — the type of job you are logging, e.g. eval, worker, ps (default: training)
      save_code — save the main python or notebook file to wandb to enable diffing (default: editable from your settings page)
      group — a string by which to group other runs; see Grouping
      reinit — whether to allow multiple calls to wandb.init in the same process (default: False)
      id — A unique ID for this run primarily used for Resuming. It must be globally unique, and if you delete a run you can't reuse the ID. Use the name field for a descriptive, useful name for the run. The ID cannot contain special characters.
      resume — if set to True, the run auto resumes; can also be a unique string for manual resuming; see Resuming (default: False)
      anonymous — can be "allow", "never", or "must". This enables or explicitly disables anonymous logging. (default: never)
      force — whether to force a user to be logged into wandb when running a script (default: False)
      magic — (bool, dict, or str, optional): magic configuration as bool, dict, json string, yaml filename. If set to True will attempt to auto-instrument your script. (default: None)
      sync_tensorboard — A boolean indicating whether or not copy all TensorBoard logs wandb; see Tensorboard (default: False)
      monitor_gym — A boolean indicating whether or not to log videos generated by OpenAI Gym; see Ray Tune (default: False)
      allow_val_change — whether to allow wandb.config values to change, by default we throw an exception if config values are overwritten. (default: False)
  
  Examples:
      Create sacred experiment::
      from wandb.sacred import WandbObserver
      ex.observers.append(WandbObserver(project='sacred_test',
                                              name='test1'))
      @ex.config
      def cfg():
          C = 1.0
          gamma = 0.7
      @ex.automain
      def run(C, gamma, _run):
          iris = datasets.load_iris()
          per = permutation(iris.target.size)
          iris.data = iris.data[per]
          iris.target = iris.target[per]
          clf = svm.SVC(C, 'rbf', gamma=gamma)
          clf.fit(iris.data[:90],
                  iris.target[:90])
          return clf.score(iris.data[90:],
                              iris.target[90:])
docstr: TODO: Maintain resources list.
docstr_tok: ["TODO",":","Maintain","resources","list","."]
code: def resource_event(self, filename):
      """TODO: Maintain resources list."""
      if filename not in self.resources:
          md5 = get_digest(filename)
          self.resources[filename] = md5
  
file_path: wandb/wandb/integration/sacred/__init__.py
imports: import warnings
  import numpy
  from sacred.dependencies import get_digest
  from sacred.observers import RunObserver
  import wandb
label_desc: Use image node_id 5 for calling the WandbObserver obj's underlying member method code with example usage: obj.resource_event(filename) without return types