\ :py:mod:`clipppy.stochastic.infinite`
=======================================

.. py:module:: clipppy.stochastic.infinite


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

.. py:class:: InfiniteUniform(batch_shape=torch.Size(), event_shape=torch.Size(), validate_args=None)



   A uniform distribution over all real numbers.

   Sampling and `log_prob` always return zeros.

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

      Returns a dictionary from argument names to
      :class:`~torch.distributions.constraints.Constraint` objects that
      should be satisfied by each argument of this distribution. Args that
      are not tensors need not appear in this dict.


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

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

      Args:
          value (Tensor):


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

      Generates a sample_shape shaped reparameterized sample or sample_shape
      shaped batch of reparameterized 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:class:: SemiInfiniteUniform(batch_shape=torch.Size(), event_shape=torch.Size(), validate_args=None)



   A uniform distribution over all real numbers.

   Sampling and `log_prob` always return zeros.

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

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

      Args:
          value (Tensor):


   .. py:method:: support(self)

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