We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
API error in 'write_raw_bids': channel type mappings description is not correct. KeyError: 'grad'
### Description of the problem


We have a Neuromag-122 system which is equipped with 61 dual-channel planar first-order gradiometers. 
I am trying to convert our local meg files to MNE-BIDS format using write_raw_bids API. The API prompts the following error. 




 

### Steps to reproduce

```Python
***Error description*:**

Traceback (most recent call last):
  File "/media/dip_linux/18F80D53F80D3094/Auditory_P3/x_Dip_p300/auditory_code/regression_model/MNE_BIDs.py", line 99, in <module>
    write_raw_bids(
  File "<decorator-gen-627>", line 12, in write_raw_bids
  File "/home/dip_linux/anaconda3/envs/mne/lib/python3.10/site-packages/mne_bids/write.py", line 1865, in write_raw_bids
    _channels_tsv(raw, channels_path.fpath, overwrite)
  File "/home/dip_linux/anaconda3/envs/mne/lib/python3.10/site-packages/mne_bids/write.py", line 126, in _channels_tsv
    ch_type.append(map_chs[_channel_type])
KeyError: 'grad'


# It seems channel type mappings description is not correct. 
Currently its written as: 
# line 90: 
map_desc.update(meggradaxial='Axial Gradiometer',
                        megrefgradaxial='Axial Gradiometer Reference',
                        meggradplanar='Planar Gradiometer',
                        megmag='Magnetometer',
                        megrefmag='Magnetometer Reference',
                        stim='Trigger',
                        eeg='ElectroEncephaloGram',
                        ecog='Electrocorticography',
                        seeg='StereoEEG',
                        ecg='ElectroCardioGram',
                        eog='ElectroOculoGram',
                        emg='ElectroMyoGram',
                        misc='Miscellaneous',
                        bio='Biological',
                        ias='Internal Active Shielding',
                        dbs='Deep Brain Stimulation',
                        fnirs_cw_amplitude='Near Infrared Spectroscopy '
                                           '(continuous wave)',
                        resp='Respiration',
                        gsr='Galvanic skin response (electrodermal activity, EDA)',
                        temperature='Temperature',)

    # get the manufacturer from the file in the Raw object
    _, ext = _parse_ext(raw.filenames[0])
    manufacturer = MANUFACTURERS.get(ext, '')
    ignored_channels = IGNORED_CHANNELS.get(manufacturer, list())

    status, ch_type, description = list(), list(), list()
    for idx, ch in enumerate(raw.info['ch_names']):
        status.append('bad' if ch in raw.info['bads'] else 'good')
        _channel_type = channel_type(raw.info, idx)
        if _channel_type in get_specific:
            _channel_type = coil_type(raw.info, idx, _channel_type)
        ch_type.append(map_chs[_channel_type])    
        description.append(map_desc[_channel_type])   # error prompt line 126
```


### Expected results

current system outputs:

map_desc output is:

defaultdict(<function _channels_tsv.<locals>.<lambda> at 0x7f7705bcadd0>, {'meggradaxial': 'Axial Gradiometer', 'megrefgradaxial': 'Axial Gradiometer Reference', 'meggradplanar': 'Planar Gradiometer', 'megmag': 'Magnetometer', 'megrefmag': 'Magnetometer Reference', 'stim': 'Trigger', 'eeg': 'ElectroEncephaloGram', 'ecog': 'Electrocorticography', 'seeg': 'StereoEEG', 'ecg': 'ElectroCardioGram', 'eog': 'ElectroOculoGram', 'emg': 'ElectroMyoGram', 'misc': 'Miscellaneous', 'bio': 'Biological', 'ias': 'Internal Active Shielding', 'dbs': 'Deep Brain Stimulation', 'fnirs_cw_amplitude': 'Near Infrared Spectroscopy (continuous wave)', 'resp': 'Respiration', 'gsr': 'Galvanic skin response (electrodermal activity, EDA)', 'temperature': 'Temperature'})

 _channel_type output is: 
'grad'


### Actual results

######### error line 126
ch_type.append(map_chs[_channel_type]) 
map_chs[_channel_type] expects the key word 'meggradplanar' instead of 'grad'


### Additional information
Platform:         Linux-5.15.0-52-generic-x86_64-with-glibc2.31
Python:           3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:35:26) [GCC 10.4.0]
Executable:       /home/dip_linux/anaconda3/envs/mne/bin/python3.10
CPU:              x86_64: 8 cores
Memory:           15.3 GB

mne:              1.2.3
numpy:            1.22.4 {OpenBLAS 0.3.21 with 8 threads}
scipy:            1.9.1
matplotlib:       3.6.0 {backend=QtAgg}

sklearn:          1.1.2
numba:            0.55.2
nibabel:          4.0.2
nilearn:          0.9.2
dipy:             1.5.0
openmeeg:         Not found
cupy:             Not found
pandas:           1.5.0
pyvista:          0.36.1 {OpenGL 3.3 (Core Profile) Mesa 21.2.6 via Mesa Intel(R) UHD Graphics (CML GT2)}
pyvistaqt:        0.9.0
ipyvtklink:       0.2.2
vtk:              9.1.0
qtpy:             2.2.0 {PyQt5=5.12.10}
ipympl:           Not found
pyqtgraph:        0.12.4
pooch:            v1.6.0

mne_bids:         0.12.dev0
mne_nirs:         Not found
mne_features:     Not found
mne_qt_browser:   0.3.2
mne_connectivity: 0.4.0
mne_icalabel:     Not found


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `mne_bids/pick.py`
Content:
```
1 """Define coil types for MEG."""
2 # Authors: Matt Sanderson <matt.sanderson@mq.edu.au>
3 #
4 # License: BSD-3-Clause
5 from mne.io.constants import FIFF
6 
7 
8 def get_coil_types():
9     """Return all known coil types.
10 
11     Returns
12     -------
13     coil_types : dict
14         The keys contain the channel types, and the values contain the
15         corresponding values in the info['chs'][idx]['kind']
16 
17     """
18     return dict(meggradaxial=(FIFF.FIFFV_COIL_KIT_GRAD,
19                               FIFF.FIFFV_COIL_CTF_GRAD,
20                               # Support for gradient-compensated data:
21                               int(FIFF.FIFFV_COIL_CTF_GRAD | (3 << 16)),
22                               int(FIFF.FIFFV_COIL_CTF_GRAD | (2 << 16)),
23                               FIFF.FIFFV_COIL_AXIAL_GRAD_5CM,
24                               FIFF.FIFFV_COIL_BABY_GRAD),
25                 megrefgradaxial=(FIFF.FIFFV_COIL_CTF_REF_GRAD,
26                                  FIFF.FIFFV_COIL_CTF_OFFDIAG_REF_GRAD,
27                                  FIFF.FIFFV_COIL_MAGNES_REF_GRAD,
28                                  FIFF.FIFFV_COIL_MAGNES_OFFDIAG_REF_GRAD),
29                 meggradplanar=(FIFF.FIFFV_COIL_VV_PLANAR_T1,
30                                FIFF.FIFFV_COIL_VV_PLANAR_T2,
31                                FIFF.FIFFV_COIL_VV_PLANAR_T3),
32                 megmag=(FIFF.FIFFV_COIL_POINT_MAGNETOMETER,
33                         FIFF.FIFFV_COIL_VV_MAG_W,
34                         FIFF.FIFFV_COIL_VV_MAG_T1,
35                         FIFF.FIFFV_COIL_VV_MAG_T2,
36                         FIFF.FIFFV_COIL_VV_MAG_T3,
37                         FIFF.FIFFV_COIL_MAGNES_MAG,
38                         FIFF.FIFFV_COIL_BABY_MAG),
39                 megrefmag=(FIFF.FIFFV_COIL_KIT_REF_MAG,
40                            FIFF.FIFFV_COIL_CTF_REF_MAG,
41                            FIFF.FIFFV_COIL_MAGNES_REF_MAG,
42                            FIFF.FIFFV_COIL_BABY_REF_MAG,
43                            FIFF.FIFFV_COIL_BABY_REF_MAG2,
44                            FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG,
45                            FIFF.FIFFV_COIL_MAGNES_REF_MAG),
46                 eeg=(FIFF.FIFFV_COIL_EEG,),
47                 misc=(FIFF.FIFFV_COIL_NONE,))
48 
49 
50 def coil_type(info, idx, ch_type='n/a'):
51     """Get coil type.
52 
53     Parameters
54     ----------
55     info : dict
56         Measurement info
57     idx : int
58         Index of channel
59     ch_type : str
60         Channel type to fall back upon if a more specific
61         type is not found
62 
63     Returns
64     -------
65     type : 'meggradaxial' | 'megrefgradaxial' | 'meggradplanar'
66            'megmag' | 'megrefmag' | 'eeg' | 'misc'
67         Type of coil
68 
69     """
70     ch = info['chs'][idx]
71     for key, values in get_coil_types().items():
72         if ch['coil_type'] in values:
73             return key
74     return ch_type
75
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/mne_bids/pick.py b/mne_bids/pick.py
--- a/mne_bids/pick.py
+++ b/mne_bids/pick.py
@@ -34,6 +34,7 @@
                         FIFF.FIFFV_COIL_VV_MAG_T1,
                         FIFF.FIFFV_COIL_VV_MAG_T2,
                         FIFF.FIFFV_COIL_VV_MAG_T3,
+                        FIFF.FIFFV_COIL_NM_122,
                         FIFF.FIFFV_COIL_MAGNES_MAG,
                         FIFF.FIFFV_COIL_BABY_MAG),
                 megrefmag=(FIFF.FIFFV_COIL_KIT_REF_MAG,

{"golden_diff": "diff --git a/mne_bids/pick.py b/mne_bids/pick.py\n--- a/mne_bids/pick.py\n+++ b/mne_bids/pick.py\n@@ -34,6 +34,7 @@\n                         FIFF.FIFFV_COIL_VV_MAG_T1,\n                         FIFF.FIFFV_COIL_VV_MAG_T2,\n                         FIFF.FIFFV_COIL_VV_MAG_T3,\n+                        FIFF.FIFFV_COIL_NM_122,\n                         FIFF.FIFFV_COIL_MAGNES_MAG,\n                         FIFF.FIFFV_COIL_BABY_MAG),\n                 megrefmag=(FIFF.FIFFV_COIL_KIT_REF_MAG,\n", "issue": "API error in 'write_raw_bids': channel type mappings description is not correct. KeyError: 'grad'\n### Description of the problem\r\n\r\n\r\nWe have a Neuromag-122 system which is equipped with 61 dual-channel planar first-order gradiometers. \r\nI am trying to convert our local meg files to MNE-BIDS format using write_raw_bids API. The API prompts the following error. \r\n\r\n\r\n\r\n\r\n \r\n\r\n### Steps to reproduce\r\n\r\n```Python\r\n***Error description*:**\r\n\r\nTraceback (most recent call last):\r\n  File \"/media/dip_linux/18F80D53F80D3094/Auditory_P3/x_Dip_p300/auditory_code/regression_model/MNE_BIDs.py\", line 99, in <module>\r\n    write_raw_bids(\r\n  File \"<decorator-gen-627>\", line 12, in write_raw_bids\r\n  File \"/home/dip_linux/anaconda3/envs/mne/lib/python3.10/site-packages/mne_bids/write.py\", line 1865, in write_raw_bids\r\n    _channels_tsv(raw, channels_path.fpath, overwrite)\r\n  File \"/home/dip_linux/anaconda3/envs/mne/lib/python3.10/site-packages/mne_bids/write.py\", line 126, in _channels_tsv\r\n    ch_type.append(map_chs[_channel_type])\r\nKeyError: 'grad'\r\n\r\n\r\n# It seems channel type mappings description is not correct. \r\nCurrently its written as: \r\n# line 90: \r\nmap_desc.update(meggradaxial='Axial Gradiometer',\r\n                        megrefgradaxial='Axial Gradiometer Reference',\r\n                        meggradplanar='Planar Gradiometer',\r\n                        megmag='Magnetometer',\r\n                        megrefmag='Magnetometer Reference',\r\n                        stim='Trigger',\r\n                        eeg='ElectroEncephaloGram',\r\n                        ecog='Electrocorticography',\r\n                        seeg='StereoEEG',\r\n                        ecg='ElectroCardioGram',\r\n                        eog='ElectroOculoGram',\r\n                        emg='ElectroMyoGram',\r\n                        misc='Miscellaneous',\r\n                        bio='Biological',\r\n                        ias='Internal Active Shielding',\r\n                        dbs='Deep Brain Stimulation',\r\n                        fnirs_cw_amplitude='Near Infrared Spectroscopy '\r\n                                           '(continuous wave)',\r\n                        resp='Respiration',\r\n                        gsr='Galvanic skin response (electrodermal activity, EDA)',\r\n                        temperature='Temperature',)\r\n\r\n    # get the manufacturer from the file in the Raw object\r\n    _, ext = _parse_ext(raw.filenames[0])\r\n    manufacturer = MANUFACTURERS.get(ext, '')\r\n    ignored_channels = IGNORED_CHANNELS.get(manufacturer, list())\r\n\r\n    status, ch_type, description = list(), list(), list()\r\n    for idx, ch in enumerate(raw.info['ch_names']):\r\n        status.append('bad' if ch in raw.info['bads'] else 'good')\r\n        _channel_type = channel_type(raw.info, idx)\r\n        if _channel_type in get_specific:\r\n            _channel_type = coil_type(raw.info, idx, _channel_type)\r\n        ch_type.append(map_chs[_channel_type])    \r\n        description.append(map_desc[_channel_type])   # error prompt line 126\r\n```\r\n\r\n\r\n### Expected results\r\n\r\ncurrent system outputs:\r\n\r\nmap_desc output is:\r\n\r\ndefaultdict(<function _channels_tsv.<locals>.<lambda> at 0x7f7705bcadd0>, {'meggradaxial': 'Axial Gradiometer', 'megrefgradaxial': 'Axial Gradiometer Reference', 'meggradplanar': 'Planar Gradiometer', 'megmag': 'Magnetometer', 'megrefmag': 'Magnetometer Reference', 'stim': 'Trigger', 'eeg': 'ElectroEncephaloGram', 'ecog': 'Electrocorticography', 'seeg': 'StereoEEG', 'ecg': 'ElectroCardioGram', 'eog': 'ElectroOculoGram', 'emg': 'ElectroMyoGram', 'misc': 'Miscellaneous', 'bio': 'Biological', 'ias': 'Internal Active Shielding', 'dbs': 'Deep Brain Stimulation', 'fnirs_cw_amplitude': 'Near Infrared Spectroscopy (continuous wave)', 'resp': 'Respiration', 'gsr': 'Galvanic skin response (electrodermal activity, EDA)', 'temperature': 'Temperature'})\r\n\r\n _channel_type output is: \r\n'grad'\r\n\r\n\r\n### Actual results\r\n\r\n######### error line 126\r\nch_type.append(map_chs[_channel_type]) \r\nmap_chs[_channel_type] expects the key word 'meggradplanar' instead of 'grad'\r\n\r\n\r\n### Additional information\r\nPlatform:         Linux-5.15.0-52-generic-x86_64-with-glibc2.31\r\nPython:           3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:35:26) [GCC 10.4.0]\r\nExecutable:       /home/dip_linux/anaconda3/envs/mne/bin/python3.10\r\nCPU:              x86_64: 8 cores\r\nMemory:           15.3 GB\r\n\r\nmne:              1.2.3\r\nnumpy:            1.22.4 {OpenBLAS 0.3.21 with 8 threads}\r\nscipy:            1.9.1\r\nmatplotlib:       3.6.0 {backend=QtAgg}\r\n\r\nsklearn:          1.1.2\r\nnumba:            0.55.2\r\nnibabel:          4.0.2\r\nnilearn:          0.9.2\r\ndipy:             1.5.0\r\nopenmeeg:         Not found\r\ncupy:             Not found\r\npandas:           1.5.0\r\npyvista:          0.36.1 {OpenGL 3.3 (Core Profile) Mesa 21.2.6 via Mesa Intel(R) UHD Graphics (CML GT2)}\r\npyvistaqt:        0.9.0\r\nipyvtklink:       0.2.2\r\nvtk:              9.1.0\r\nqtpy:             2.2.0 {PyQt5=5.12.10}\r\nipympl:           Not found\r\npyqtgraph:        0.12.4\r\npooch:            v1.6.0\r\n\r\nmne_bids:         0.12.dev0\r\nmne_nirs:         Not found\r\nmne_features:     Not found\r\nmne_qt_browser:   0.3.2\r\nmne_connectivity: 0.4.0\r\nmne_icalabel:     Not found\r\n\n", "before_files": [{"content": "\"\"\"Define coil types for MEG.\"\"\"\n# Authors: Matt Sanderson <matt.sanderson@mq.edu.au>\n#\n# License: BSD-3-Clause\nfrom mne.io.constants import FIFF\n\n\ndef get_coil_types():\n    \"\"\"Return all known coil types.\n\n    Returns\n    -------\n    coil_types : dict\n        The keys contain the channel types, and the values contain the\n        corresponding values in the info['chs'][idx]['kind']\n\n    \"\"\"\n    return dict(meggradaxial=(FIFF.FIFFV_COIL_KIT_GRAD,\n                              FIFF.FIFFV_COIL_CTF_GRAD,\n                              # Support for gradient-compensated data:\n                              int(FIFF.FIFFV_COIL_CTF_GRAD | (3 << 16)),\n                              int(FIFF.FIFFV_COIL_CTF_GRAD | (2 << 16)),\n                              FIFF.FIFFV_COIL_AXIAL_GRAD_5CM,\n                              FIFF.FIFFV_COIL_BABY_GRAD),\n                megrefgradaxial=(FIFF.FIFFV_COIL_CTF_REF_GRAD,\n                                 FIFF.FIFFV_COIL_CTF_OFFDIAG_REF_GRAD,\n                                 FIFF.FIFFV_COIL_MAGNES_REF_GRAD,\n                                 FIFF.FIFFV_COIL_MAGNES_OFFDIAG_REF_GRAD),\n                meggradplanar=(FIFF.FIFFV_COIL_VV_PLANAR_T1,\n                               FIFF.FIFFV_COIL_VV_PLANAR_T2,\n                               FIFF.FIFFV_COIL_VV_PLANAR_T3),\n                megmag=(FIFF.FIFFV_COIL_POINT_MAGNETOMETER,\n                        FIFF.FIFFV_COIL_VV_MAG_W,\n                        FIFF.FIFFV_COIL_VV_MAG_T1,\n                        FIFF.FIFFV_COIL_VV_MAG_T2,\n                        FIFF.FIFFV_COIL_VV_MAG_T3,\n                        FIFF.FIFFV_COIL_MAGNES_MAG,\n                        FIFF.FIFFV_COIL_BABY_MAG),\n                megrefmag=(FIFF.FIFFV_COIL_KIT_REF_MAG,\n                           FIFF.FIFFV_COIL_CTF_REF_MAG,\n                           FIFF.FIFFV_COIL_MAGNES_REF_MAG,\n                           FIFF.FIFFV_COIL_BABY_REF_MAG,\n                           FIFF.FIFFV_COIL_BABY_REF_MAG2,\n                           FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG,\n                           FIFF.FIFFV_COIL_MAGNES_REF_MAG),\n                eeg=(FIFF.FIFFV_COIL_EEG,),\n                misc=(FIFF.FIFFV_COIL_NONE,))\n\n\ndef coil_type(info, idx, ch_type='n/a'):\n    \"\"\"Get coil type.\n\n    Parameters\n    ----------\n    info : dict\n        Measurement info\n    idx : int\n        Index of channel\n    ch_type : str\n        Channel type to fall back upon if a more specific\n        type is not found\n\n    Returns\n    -------\n    type : 'meggradaxial' | 'megrefgradaxial' | 'meggradplanar'\n           'megmag' | 'megrefmag' | 'eeg' | 'misc'\n        Type of coil\n\n    \"\"\"\n    ch = info['chs'][idx]\n    for key, values in get_coil_types().items():\n        if ch['coil_type'] in values:\n            return key\n    return ch_type\n", "path": "mne_bids/pick.py"}], "after_files": [{"content": "\"\"\"Define coil types for MEG.\"\"\"\n# Authors: Matt Sanderson <matt.sanderson@mq.edu.au>\n#\n# License: BSD-3-Clause\nfrom mne.io.constants import FIFF\n\n\ndef get_coil_types():\n    \"\"\"Return all known coil types.\n\n    Returns\n    -------\n    coil_types : dict\n        The keys contain the channel types, and the values contain the\n        corresponding values in the info['chs'][idx]['kind']\n\n    \"\"\"\n    return dict(meggradaxial=(FIFF.FIFFV_COIL_KIT_GRAD,\n                              FIFF.FIFFV_COIL_CTF_GRAD,\n                              # Support for gradient-compensated data:\n                              int(FIFF.FIFFV_COIL_CTF_GRAD | (3 << 16)),\n                              int(FIFF.FIFFV_COIL_CTF_GRAD | (2 << 16)),\n                              FIFF.FIFFV_COIL_AXIAL_GRAD_5CM,\n                              FIFF.FIFFV_COIL_BABY_GRAD),\n                megrefgradaxial=(FIFF.FIFFV_COIL_CTF_REF_GRAD,\n                                 FIFF.FIFFV_COIL_CTF_OFFDIAG_REF_GRAD,\n                                 FIFF.FIFFV_COIL_MAGNES_REF_GRAD,\n                                 FIFF.FIFFV_COIL_MAGNES_OFFDIAG_REF_GRAD),\n                meggradplanar=(FIFF.FIFFV_COIL_VV_PLANAR_T1,\n                               FIFF.FIFFV_COIL_VV_PLANAR_T2,\n                               FIFF.FIFFV_COIL_VV_PLANAR_T3),\n                megmag=(FIFF.FIFFV_COIL_POINT_MAGNETOMETER,\n                        FIFF.FIFFV_COIL_VV_MAG_W,\n                        FIFF.FIFFV_COIL_VV_MAG_T1,\n                        FIFF.FIFFV_COIL_VV_MAG_T2,\n                        FIFF.FIFFV_COIL_VV_MAG_T3,\n                        FIFF.FIFFV_COIL_NM_122,\n                        FIFF.FIFFV_COIL_MAGNES_MAG,\n                        FIFF.FIFFV_COIL_BABY_MAG),\n                megrefmag=(FIFF.FIFFV_COIL_KIT_REF_MAG,\n                           FIFF.FIFFV_COIL_CTF_REF_MAG,\n                           FIFF.FIFFV_COIL_MAGNES_REF_MAG,\n                           FIFF.FIFFV_COIL_BABY_REF_MAG,\n                           FIFF.FIFFV_COIL_BABY_REF_MAG2,\n                           FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG,\n                           FIFF.FIFFV_COIL_MAGNES_REF_MAG),\n                eeg=(FIFF.FIFFV_COIL_EEG,),\n                misc=(FIFF.FIFFV_COIL_NONE,))\n\n\ndef coil_type(info, idx, ch_type='n/a'):\n    \"\"\"Get coil type.\n\n    Parameters\n    ----------\n    info : dict\n        Measurement info\n    idx : int\n        Index of channel\n    ch_type : str\n        Channel type to fall back upon if a more specific\n        type is not found\n\n    Returns\n    -------\n    type : 'meggradaxial' | 'megrefgradaxial' | 'meggradplanar'\n           'megmag' | 'megrefmag' | 'eeg' | 'misc'\n        Type of coil\n\n    \"\"\"\n    ch = info['chs'][idx]\n    for key, values in get_coil_types().items():\n        if ch['coil_type'] in values:\n            return key\n    return ch_type\n", "path": "mne_bids/pick.py"}]}