<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Plot a cortical parcellation
In this example, we download the HCP-MMP1.0 parcellation
Step1:  We can also plot a combined set of labels (23 per hemisphere).
Step2:  We can add another custom parcellation
<ASSISTANT_TASK:>
Python Code:
# Author: Eric Larson <larson.eric.d@gmail.com>
#         Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
Brain = mne.viz.get_brain_class()
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
                                        verbose=True)
mne.datasets.fetch_aparc_sub_parcellation(subjects_dir=subjects_dir,
                                          verbose=True)
labels = mne.read_labels_from_annot(
    'fsaverage', 'HCPMMP1', 'lh', subjects_dir=subjects_dir)
brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
              cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('HCPMMP1')
aud_label = [label for label in labels if label.name == 'L_A1_ROI-lh'][0]
brain.add_label(aud_label, borders=False)
Explanation: Plot a cortical parcellation
In this example, we download the HCP-MMP1.0 parcellation
:footcite:GlasserEtAl2016 and show it on fsaverage.
We will also download the customized 448-label aparc
parcellation from :footcite:KhanEtAl2018.
<div class="alert alert-info"><h4>Note</h4><p>The HCP-MMP dataset has license terms restricting its use.
          Of particular relevance:
              "I will acknowledge the use of WU-Minn HCP data and data
              derived from WU-Minn HCP data when publicly presenting any
              results or algorithms that benefitted from their use."</p></div>
End of explanation
brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
              cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('HCPMMP1_combined')
Explanation: We can also plot a combined set of labels (23 per hemisphere).
End of explanation
brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
              cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('aparc_sub')
Explanation: We can add another custom parcellation
End of explanation
<END_TASK>