ValueError: Unused step method arguments: {'initvals'}

I am trying to use Automated Recommendation Tool which utilizes pymc3 package but I am facing issues while executing the following code cell -

%%time
if run_art:
    art = RecommendationEngine(df, **art_params)
else:
    with open(os.path.join(art_params['output_directory'], 'art.pkl'), 'rb') as output:
        art = pickle.load(output)

Following is the error that pops up -

ValueError                                Traceback (most recent call last)
File <timed exec>:2, in <module>

File ~/MyDrive/BII/AutomatedRecommendationTool/art/core.py:409, in RecommendationEngine.__init__(self, df, input_vars, input_var_type, bounds_file, scale_input_vars, response_vars, build_model, cross_val, ensemble_model, standardize, intercept, recommend, objective, threshold, target_values, num_recommendations, rel_rec_distance, niter, alpha, output_directory, max_mcmc_cores, verbose, testing, seed, initial_cycle, warning_callback, last_dashes_denote_replicates, num_sklearn_models, num_tpot_models)
    407     self.save_pkl_object()
    408 elif build_model:
--> 409     self.build_model()
    410     if recommend:
    411         self.optimize()

File ~/MyDrive/BII/AutomatedRecommendationTool/art/core.py:612, in RecommendationEngine.build_model(self)
    609 self._initialize_models()
    611 if self.cross_val:
--> 612     self._cross_val_models()
    613     plot.predictions_vs_observations(self, cv_flag=True, errorbars_flag=True)
    615 self._fit_models()

File ~/MyDrive/BII/AutomatedRecommendationTool/art/core.py:1063, in RecommendationEngine._cross_val_models(self)
   1056         cv_predictions[j][i] = level0_cv_predictions
   1058 # ================================================== #
   1059 # Cross validated predictions for the ensemble model
   1060 # -------------------------------------------------- #
   1061 
   1062 # Build (fit) ensemble model
-> 1063 self._build_ensemble_model(idx=train_idx)
   1065 # Predictions with ensemble model
   1066 # Apart from the mean values, store prediction std and draws for plotting
   1067 # (not possible always due to a bug in pymc3)
   1068 f = np.zeros((len(test_idx), self.num_models, self.num_response_var))

File ~/MyDrive/BII/AutomatedRecommendationTool/art/core.py:989, in RecommendationEngine._build_ensemble_model(self, idx)
    986 if self.standardize:
    987     self._standardize_level1_data()
--> 989 self._ensemble_model(idx)

File ~/MyDrive/BII/AutomatedRecommendationTool/art/core.py:1428, in RecommendationEngine._ensemble_model(self, idx, testing)
   1418 if not testing:
   1419     # Instantiate sampler and draw samples from the posterior.
   1420     # Omit the random_seed parameter, since PYMC3 @3.8 internally calls
   (...)
   1425     # chains.  That should still be predictable since ART calls np.random.seed()
   1426     # above.
   1427     step = pm.NUTS()  # Slice, Metropolis, HamiltonianMC, NUTS
-> 1428     self.trace[j] = pm.sample(
   1429         const.n_iterations,
   1430         step=step,
   1431         initvals=initvals,
   1432         progressbar=progressbar,
   1433         tune=const.tune_steps,
   1434         cores=cores,
   1435         # work around an API update to be added in PYMC3 4.0
   1436         return_inferencedata=False,
   1437         # ,  init=adapt_diag
   1438         # live_plot=True, skip_first=100, refresh_every=300, roll_over=1000
   1439     )
   1441     logger = logging.getLogger("pymc3")
   1442     logger.propagate = True

File ~/anaconda3/lib/python3.9/site-packages/pymc3/sampling.py:515, in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)
    513         step = assign_step_methods(model, step, step_kwargs=kwargs)
    514 else:
--> 515     step = assign_step_methods(model, step, step_kwargs=kwargs)
    517 if isinstance(step, list):
    518     step = CompoundStep(step)

File ~/anaconda3/lib/python3.9/site-packages/pymc3/sampling.py:217, in assign_step_methods(model, step, methods, step_kwargs)
    209         selected = max(
    210             methods,
    211             key=lambda method, var=var, has_gradient=has_gradient: method._competence(
    212                 var, has_gradient
    213             ),
    214         )
    215         selected_steps[selected].append(var)
--> 217 return instantiate_steppers(model, steps, selected_steps, step_kwargs)

File ~/anaconda3/lib/python3.9/site-packages/pymc3/sampling.py:143, in instantiate_steppers(_model, steps, selected_steps, step_kwargs)
    141 unused_args = set(step_kwargs).difference(used_keys)
    142 if unused_args:
--> 143     raise ValueError("Unused step method arguments: %s" % unused_args)
    145 if len(steps) == 1:
    146     return steps[0]

ValueError: Unused step method arguments: {'initvals'}

I am using Ubuntu as OS and I have already installed a C++ compiler.
Anybody who has faced similar issue and has been able to solve it, please help me out.