Parameters: 
 info : dict
The measurement info to specify the channels to include.
Bad channels in info[‘bads’] are not used.
 forward : dict
Forward operator.
 data_cov : Covariance
The data covariance.
 reg : float
The regularization for the whitened data covariance.
 noise_cov : Covariance
The noise covariance. If provided, whitening will be done. Providing a
noise covariance is mandatory if you mix sensor types, e.g.
gradiometers with magnetometers or EEG with MEG.
 label : Label
Restricts the LCMV solution to a given label.
 pick_ori : None  ‘normal’  ‘maxpower’  ‘vector’
For forward solutions with fixed orientation, None (default) must be
used and a scalar beamformer is computed. For freeorientation forward
solutions, a vector beamformer is computed and:
 None
Pools the orientations by taking the norm.
 ‘normal’
Keeps only the radial component.
 ‘maxpower’
Selects orientations that maximize output source power at
each location.
 ‘vector’
Keeps the currents for each direction separate
 rank : int  None  ‘full’
This controls the effective rank of the covariance matrix when
computing the inverse. The rank can be set explicitly by specifying an
integer value. If None , the rank will be automatically estimated.
Since applying regularization will always make the covariance matrix
full rank, the rank is estimated before regularization in this case. If
‘full’, the rank will be estimated after regularization and hence
will mean using the full rank, unless reg=0 is used.
The default in 'full' .
 weight_norm : ‘unitnoisegain’  ‘nai’  None
If ‘unitnoisegain’, the unitnoise gain minimum variance beamformer
will be computed (BorgiottiKaplan beamformer) [2],
if ‘nai’, the Neural Activity Index [1] will be computed,
if None, the unitgain LCMV beamformer [2] will be computed.
 reduce_rank : bool
If True, the rank of the leadfield will be reduced by 1 for each
spatial location. Setting reduce_rank to True is typically necessary
if you use a single sphere model for MEG.
 verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).
