code: def _mc_fit(indexed_params, estimator, metrics, prng):
      # unpack params
      index, (nn, (cov, prec, adj), lam, n_samples) = indexed_params
  
      # compute mc trial
      X = _sample_mvn(n_samples, cov, prng)
      mc_estimator = clone(estimator)
      mc_estimator.set_params(lam=lam)
      mc_estimator.fit(X)
      results = {
          k: f(prec, mc_estimator.precision_)
          for k, f in metrics.items()
      }
  
      return index, results
  
file_path: skggm/inverse_covariance/profiling/monte_carlo_profile.py
imports: from __future__ import absolute_import
  import numpy
  from functools import partial
  from sklearn.base import clone
  from joblib import Parallel, delayed
  from .metrics import error_fro
  from .erdos_renyi_graph import ErdosRenyiGraph
  from ..None import QuicGraphicalLasso, QuicGraphicalLassoCV
label_desc: Use image node_id 3 for calling a global function with example usage: _mc_fit(indexed_params, estimator, metrics, prng) and returns: index,  results