code: def _get_minimal_mcmc_kwargs(**kwargs: Any) -> Dict[str, Any]:
      kwargs["warmup_steps"] = 0
      # Just get as many samples as otherwise expected.
      kwargs["num_samples"] = kwargs.get(
          "num_samples", 256
      ) // kwargs.get("thinning", 16)
      kwargs["thinning"] = 1
      return kwargs
  
file_path: Ax/ax/utils/testing/mock.py
imports: from contextlib import contextmanager, ExitStack
  from functools import wraps
  from typing import Any, Callable, Dict, Generator, Optional
  from unittest import mock
  from ax.models.torch.fully_bayesian import run_inference
  from botorch.fit import fit_fully_bayesian_model_nuts
  from botorch.generation.gen import minimize_with_timeout
  from botorch.optim.initializers import gen_batch_initial_conditions, gen_one_shot_kg_initial_conditions
  from scipy.optimize import OptimizeResult
  from torch import Tensor
label_desc: Use image node_id 5 for calling a global function with example usage: _get_minimal_mcmc_kwargs() and returns: kwargs