from .diagnostics import effective_sample_size as ess
from .diagnostics import potential_scale_reduction as rhat
from .kernels import (
    adaptive_tempered_smc,
    elliptical_slice,
    ghmc,
    hmc,
    irmh,
    mala,
    meads,
    mgrad_gaussian,
    nuts,
    orbital_hmc,
    pathfinder_adaptation,
    rmh,
    sghmc,
    sgld,
    tempered_smc,
    window_adaptation,
)
from .optimizers import dual_averaging, lbfgs

__all__ = [
    "dual_averaging",  # optimizers
    "lbfgs",
    "hmc",  # mcmc
    "mala",
    "mgrad_gaussian",
    "nuts",
    "orbital_hmc",
    "rmh",
    "irmh",
    "elliptical_slice",
    "ghmc",
    "meads",
    "sgld",  # stochastic gradient mcmc
    "sghmc",
    "window_adaptation",  # mcmc adaptation
    "pathfinder_adaptation",
    "adaptive_tempered_smc",  # smc
    "tempered_smc",
    "ess",  # diagnostics
    "rhat",
]

from . import _version

__version__ = _version.get_versions()["version"]

from .diagnostics import effective_sample_size as ess
from .diagnostics import potential_scale_reduction as rhat
from .kernels import (
    adaptive_tempered_smc,
    elliptical_slice,
    ghmc,
    hmc,
    irmh,
    mala,
    meads,
    mgrad_gaussian,
    nuts,
    orbital_hmc,
    pathfinder,
    pathfinder_adaptation,
    rmh,
    sghmc,
    sgld,
    tempered_smc,
    window_adaptation,
)
from .optimizers import dual_averaging, lbfgs

__all__ = [
    "dual_averaging",  # optimizers
    "lbfgs",
    "hmc",  # mcmc
    "mala",
    "mgrad_gaussian",
    "nuts",
    "orbital_hmc",
    "rmh",
    "irmh",
    "elliptical_slice",
    "ghmc",
    "meads",
    "sgld",  # stochastic gradient mcmc
    "sghmc",
    "window_adaptation",  # mcmc adaptation
    "pathfinder_adaptation",
    "adaptive_tempered_smc",  # smc
    "tempered_smc",
    "pathfinder",  # variational inference
    "ess",  # diagnostics
    "rhat",
]

from . import _version

__version__ = _version.get_versions()["version"]

Add `pathfinder` to the library namespace
Add `pathfinder` to the library namespace

  from .diagnostics import effective_sample_size as ess
  from .diagnostics import potential_scale_reduction as rhat
  from .kernels import (
      adaptive_tempered_smc,
      elliptical_slice,
      ghmc,
      hmc,
      irmh,
      mala,
      meads,
      mgrad_gaussian,
      nuts,
      orbital_hmc,
+     pathfinder,
      pathfinder_adaptation,
      rmh,
      sghmc,
      sgld,
      tempered_smc,
      window_adaptation,
  )
  from .optimizers import dual_averaging, lbfgs
  
  __all__ = [
      "dual_averaging",  # optimizers
      "lbfgs",
      "hmc",  # mcmc
      "mala",
      "mgrad_gaussian",
      "nuts",
      "orbital_hmc",
      "rmh",
      "irmh",
      "elliptical_slice",
      "ghmc",
      "meads",
      "sgld",  # stochastic gradient mcmc
      "sghmc",
      "window_adaptation",  # mcmc adaptation
      "pathfinder_adaptation",
      "adaptive_tempered_smc",  # smc
      "tempered_smc",
+     "pathfinder",  # variational inference
      "ess",  # diagnostics
      "rhat",
  ]
  
  from . import _version
  
  __version__ = _version.get_versions()["version"]
  
Add `pathfinder` to the library namespace
## Code Before:
from .diagnostics import effective_sample_size as ess
from .diagnostics import potential_scale_reduction as rhat
from .kernels import (
    adaptive_tempered_smc,
    elliptical_slice,
    ghmc,
    hmc,
    irmh,
    mala,
    meads,
    mgrad_gaussian,
    nuts,
    orbital_hmc,
    pathfinder_adaptation,
    rmh,
    sghmc,
    sgld,
    tempered_smc,
    window_adaptation,
)
from .optimizers import dual_averaging, lbfgs

__all__ = [
    "dual_averaging",  # optimizers
    "lbfgs",
    "hmc",  # mcmc
    "mala",
    "mgrad_gaussian",
    "nuts",
    "orbital_hmc",
    "rmh",
    "irmh",
    "elliptical_slice",
    "ghmc",
    "meads",
    "sgld",  # stochastic gradient mcmc
    "sghmc",
    "window_adaptation",  # mcmc adaptation
    "pathfinder_adaptation",
    "adaptive_tempered_smc",  # smc
    "tempered_smc",
    "ess",  # diagnostics
    "rhat",
]

from . import _version

__version__ = _version.get_versions()["version"]

## Instruction:
Add `pathfinder` to the library namespace
## Code After:
from .diagnostics import effective_sample_size as ess
from .diagnostics import potential_scale_reduction as rhat
from .kernels import (
    adaptive_tempered_smc,
    elliptical_slice,
    ghmc,
    hmc,
    irmh,
    mala,
    meads,
    mgrad_gaussian,
    nuts,
    orbital_hmc,
    pathfinder,
    pathfinder_adaptation,
    rmh,
    sghmc,
    sgld,
    tempered_smc,
    window_adaptation,
)
from .optimizers import dual_averaging, lbfgs

__all__ = [
    "dual_averaging",  # optimizers
    "lbfgs",
    "hmc",  # mcmc
    "mala",
    "mgrad_gaussian",
    "nuts",
    "orbital_hmc",
    "rmh",
    "irmh",
    "elliptical_slice",
    "ghmc",
    "meads",
    "sgld",  # stochastic gradient mcmc
    "sghmc",
    "window_adaptation",  # mcmc adaptation
    "pathfinder_adaptation",
    "adaptive_tempered_smc",  # smc
    "tempered_smc",
    "pathfinder",  # variational inference
    "ess",  # diagnostics
    "rhat",
]

from . import _version

__version__ = _version.get_versions()["version"]
