How to use the tigramite.pcmci.PCMCI function in tigramite

def a_pcmci(a_sample, a_test, a_common_params, request):
    # Unpack the test data and true parent graph
    dataframe, true_parents = a_sample
    # Unpack the common parameters
    tau_min, tau_max, sel_link = a_common_params
    # Get the parameters from this request
    select_vars = request.param
    # Build the PCMCI instance
    pcmci = PCMCI(selected_variables=select_vars,
                  dataframe=dataframe,
                  cond_ind_test=a_test,
                  verbosity=VERBOSITY)
    # If there are selected variables, edit the true parents to reflect this
    if select_vars is not None:
        true_parents = {sel_v : true_parents[sel_v] for sel_v in select_vars}
    # Select the correct links if they are given
    select_links = _select_links(sel_link, true_parents)
    # Ensure we change the true parents to be the same as the selected links
    if select_links is not None:
        true_parents = select_links
    # Return the constructed PCMCI, expected results, and common parameters
    return pcmci, true_parents, tau_min, tau_max, select_links

def a_pcmci(a_sample, request):
    # Unpack the test data and true parent graph
    dataframe, true_parents = a_sample
    # Build the PCMCI instance
    pcmci = PCMCI(selected_variables=None,
                  dataframe=dataframe,
                  cond_ind_test=ParCorr(verbosity=VERBOSITY),
                  verbosity=VERBOSITY)
    # Return the constructed PCMCI, expected results, and common parameters
    return pcmci, true_parents

# Cache the resulting values in the return dictionary
        return_dict = {'val_matrix': val_matrix,
                       'p_matrix': p_matrix,
                       'q_matrix': q_matrix,
                       'conf_matrix': conf_matrix}
        # Print the information
        if self.verbosity > 0:
            self.print_results(return_dict)
        # Return the dictionary
        return return_dict

if __name__ == '__main__':
    from tigramite.independence_tests import ParCorr
    import tigramite.data_processing as pp
    dataframe = pp.DataFrame(np.random.randn(100,3),)
    pcmci = PCMCI(dataframe, ParCorr())

    pcmci.get_corrected_pvalues(np.random.rand(2,2,2))

Parameters
    ----------
    j : int
        Variable index.

    Returns
    -------
    j, pcmci_of_j, parents_of_j : tuple
        Variable index, PCMCI object, and parents of j
    """

    # CondIndTest is initialized globally below
    # Further parameters of PCMCI as described in the documentation can be
    # supplied here:
    pcmci_of_j = PCMCI(
        dataframe=dataframe,
        cond_ind_test=cond_ind_test,
        selected_variables=[j],
        verbosity=verbosity)

    # Run PC condition-selection algorithm. Also here further parameters can be
    # specified:
    parents_of_j = pcmci_of_j.run_pc_stable(
                      selected_links=selected_links,
                      tau_max=tau_max,
                      pc_alpha=pc_alpha,
            )

    # We return also the PCMCI object because it may contain pre-computed 
    # results can be re-used in the MCI step (such as residuals or null
    # distributions)

dataframe=self.dataframe,
                        model=prediction_model,
                        data_transform=data_transform,
                        mask_type='y',
                        verbosity=verbosity)

        # Build the testing dataframe as well
        self.test_mask = np.copy(mask)
        self.test_mask[[t for t in range(T) if t not in test_indices]] = True

        # Setup the PCMCI instance
        if cond_ind_test is not None:
            # Force the masking
            cond_ind_test.set_mask_type('y')
            cond_ind_test.verbosity = verbosity
            PCMCI.__init__(self,
                           dataframe=self.dataframe,
                           cond_ind_test=cond_ind_test,
                           selected_variables=None,
                           var_names=None,
                           verbosity=verbosity)

        # Set the member variables
        self.cond_ind_test = cond_ind_test
        # Initialize member varialbes that are set outside
        self.target_predictors = None
        self.selected_targets = None
        self.fitted_model = None
        self.test_array = None