9a06cce3a455541e3f606552560c9efa4830c9b8,Tars/distributions/distribution_models.py,DistributionModel,sample,#DistributionModel#Any#Any#Any#Any#Any#,55

Before Change

                {self.var[0]: self._get_sample(reparam=reparam,
                                               sample_shape=sample_shape)}

        elif x is not None:  // conditional
            if type(x) is torch.Tensor:
                x = {self.cond_var[0]: x}

            elif type(x) is list:
                x = dict(zip(self.cond_var, x))

            elif type(x) is dict:
                if not set(list(x.keys())) == set(self.cond_var):
                    raise ValueError("Input"s keys are not valid.")

            else:
                raise ValueError("Invalid input")

            params = self._get_forward(x)
            self._set_dist(params)

After Change

        else:  // conditional
            x = self._verify_input(x)
            params = self.forward(**x)
            print(params)
            self._set_dist(**params)

            output = {self.var[0]: self._get_sample(reparam=reparam)}

Instances