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OriolAbriltwiecki
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add missing objects and modules to docs
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docs/source/api.rst

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api/distributions
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api/gp
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api/model
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api/ode
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api/samplers
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api/vi
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api/smc
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api/step_methods
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api/inference
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api/backends
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api/data
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api/bart
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api/ode
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api/tuning
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api/math
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api/aesaraf
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api/shape_utils
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api/misc
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--------------
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API extensions

docs/source/api/aesaraf.rst

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Aesara utils
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************
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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gradient
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hessian
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hessian_diag
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inputvars
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cont_inputs
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floatX
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intX
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smartfloatX
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jacobian
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CallableTensor
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join_nonshared_inputs
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make_shared_replacements
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generator
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set_at_rng
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at_rng
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take_along_axis
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pandas_to_array

docs/source/api/backends.rst

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Storage backends
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****************
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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to_inference_data
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predictions_to_inference_data
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Internal structures
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-------------------
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.. automodule:: pymc.backends
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.. autosummary::
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:toctree: generated/
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NDArray
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point_list_to_multitrace
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base.BaseTrace
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base.MultiTrace

docs/source/api/bart.rst

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Bayesian Additive Regression Trees (BART)
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*****************************************
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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BART
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PGBART
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bart.plot_dependence
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bart.predict

docs/source/api/data.rst

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Data
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****
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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ConstantData
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MutableData
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get_data
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Data
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GeneratorAdapter
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Minibatch
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align_minibatches

docs/source/api/inference.rst

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docs/source/api/math.rst

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variables, or defining custom likelihoods or priors requires you to use these Aesara
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expressions rather than NumPy or Python code.
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.. currentmodule:: pymc.math
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.. currentmodule:: pymc
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Functions exposed in pymc namespace
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-----------------------------------
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.. autosummary::
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:toctree: generated/
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expand_packed_triangular
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logit
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invlogit
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probit
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invprobit
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logsumexp
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Functions exposed in pymc.math
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------------------------------
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.. automodule:: pymc.math
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.. autosummary::
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:toctree: generated/
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dot
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constant
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flatten
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logsumexp
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invlogit
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logit
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.. automodule:: pymc.math
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:members:

docs/source/api/misc.rst

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Other utils
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***********
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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find_constrained_prior
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DictToArrayBijection
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Printing
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--------
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.. autosummary::
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:toctree: generated/
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str_for_dist
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str_for_model
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str_for_potential_or_deterministic

docs/source/api/model.rst

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Model
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-----
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.. currentmodule:: pymc.model
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.. automodule:: pymc.model
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:members:
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Model creation and inspection
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-----------------------------
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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Model
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model_to_graphviz
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modelcontext
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Others
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------
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.. autosummary::
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:toctree: generated/
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Deterministic
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Potential
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set_data
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Point
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compile_fn

docs/source/api/ode.rst

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Ordinary differential equations (ODEs)
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**************************************
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This submodule contains tools used to perform inference on ordinary differential equations.
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.. currentmodule:: pymc.ode
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.. automodule:: pymc.ode
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.. autosummary::
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:toctree: generated/
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.. automodule:: pymc.ode
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:members: DifferentialEquation
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DifferentialEquation

docs/source/api/samplers.rst

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====
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Samplers
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====
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========
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This submodule contains functions for MCMC sampling.
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This submodule contains functions for MCMC and forward sampling.
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.. currentmodule:: pymc.sampling
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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.. automodule:: pymc.sampling
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:members:
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sample
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sample_prior_predictive
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sample_posterior_predictive
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sample_posterior_predictive_w
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iter_sample
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init_nuts
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draw
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Step methods
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************
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.. currentmodule:: pymc
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HMC family
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----------
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.. autosummary::
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:toctree: generated/
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NUTS
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HamiltonianMC
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Metropolis family
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-----------------
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.. autosummary::
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:toctree: generated/
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BinaryGibbsMetropolis
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BinaryMetropolis
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CategoricalGibbsMetropolis
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CauchyProposal
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DEMetropolis
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DEMetropolisZ
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LaplaceProposal
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Metropolis
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MultivariateNormalProposal
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NormalProposal
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PoissonProposal
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UniformProposal
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MLDA family
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-----------
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.. autosummary::
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:toctree: generated/
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MLDA
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DEMetropolisZMLDA
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MetropolisMLDA
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RecursiveDAProposal
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Other step methods
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------------------
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.. autosummary::
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:toctree: generated/
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CompoundStep
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EllipticalSlice
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Slice

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