161ae41bebc73c146627169f761e3c4ddf83e5d4,pixyz/losses/losses.py,Expectation,__init__,#Expectation#Any#Any#Any#Any#Any#,745

Before Change

        super().__init__(input_var=input_var)
        self.p = p
        self.f = f
        self.sample_shape = torch.Size(sample_shape)
        self.reparam = reparam

    @property
    def _symbol(self):

After Change

class Loss(torch.nn.Module, metaclass=abc.ABCMeta):
    Loss class. In Pixyz, all loss classes are required to inherit this class.

    Examples
    --------
    >>> import torch
    >>> from torch.nn import functional as F
    >>> from pixyz.distributions import Bernoulli, Normal
    >>> from pixyz.losses import KullbackLeibler
    ...
    >>> // Set distributions
    >>> class Inference(Normal):
    ...     def __init__(self):
    ...         super().__init__(cond_var=["x"], var=["z"], name="q")
    ...         self.model_loc = torch.nn.Linear(128, 64)

Instances