\ :py:mod:`clipppy.utils.distributions.wrapper`
===============================================

.. py:module:: clipppy.utils.distributions.wrapper


Module Contents
---------------

.. py:data:: _Distribution
   

   

.. py:data:: _size
   

   

.. py:class:: DistributionWrapper(base_dist: _Distribution, batch_shape: _size = None, event_shape: _size = None, validate_args=None)



   Base class for a distribution that delegates to another one.

   .. py:attribute:: arg_constraints
      

      

   .. py:method:: batch_dim(self)
      :property:


   .. py:method:: cdf(self, value)

      Returns the cumulative density/mass function evaluated at
      `value`.

      Args:
          value (Tensor):


   .. py:method:: entropy(self)

      Returns entropy of distribution, batched over batch_shape.

      Returns:
          Tensor of shape batch_shape.


   .. py:method:: enumerate_support(self, expand=True)

      Returns tensor containing all values supported by a discrete
      distribution. The result will enumerate over dimension 0, so the shape
      of the result will be `(cardinality,) + batch_shape + event_shape`
      (where `event_shape = ()` for univariate distributions).

      Note that this enumerates over all batched tensors in lock-step
      `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens
      along dim 0, but with the remaining batch dimensions being
      singleton dimensions, `[[0], [1], ..`.

      To iterate over the full Cartesian product use
      `itertools.product(m.enumerate_support())`.

      Args:
          expand (bool): whether to expand the support over the
              batch dims to match the distribution's `batch_shape`.

      Returns:
          Tensor iterating over dimension 0.


   .. py:method:: expand(self, batch_shape, _instance=None)

      Returns a new distribution instance (or populates an existing instance
      provided by a derived class) with batch dimensions expanded to
      `batch_shape`. This method calls :class:`~torch.Tensor.expand` on
      the distribution's parameters. As such, this does not allocate new
      memory for the expanded distribution instance. Additionally,
      this does not repeat any args checking or parameter broadcasting in
      `__init__.py`, when an instance is first created.

      Args:
          batch_shape (torch.Size): the desired expanded size.
          _instance: new instance provided by subclasses that
              need to override `.expand`.

      Returns:
          New distribution instance with batch dimensions expanded to
          `batch_size`.


   .. py:method:: has_enumerate_support(self)
      :property:


   .. py:method:: has_rsample(self)
      :property:


   .. py:method:: icdf(self, value)

      Returns the inverse cumulative density/mass function evaluated at
      `value`.

      Args:
          value (Tensor):


   .. py:method:: log_prob(self, value)

      Returns the log of the probability density/mass function evaluated at
      `value`.

      Args:
          value (Tensor):


   .. py:method:: mean(self)
      :property:

      Returns the mean of the distribution.


   .. py:method:: rsample(self, sample_shape=Size())

      Generates a sample_shape shaped reparameterized sample or sample_shape
      shaped batch of reparameterized samples if the distribution parameters
      are batched.


   .. py:method:: sample(self, sample_shape=Size())

      Generates a sample_shape shaped sample or sample_shape shaped batch of
      samples if the distribution parameters are batched.


   .. py:method:: support(self)

      Returns a :class:`~torch.distributions.constraints.Constraint` object
      representing this distribution's support.


   .. py:method:: variance(self)
      :property:

      Returns the variance of the distribution.