botorch/posteriors/deterministic.py [65:91]:
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    def rsample(
        self,
        sample_shape: Optional[torch.Size] = None,
        base_samples: Optional[Tensor] = None,
    ) -> Tensor:
        r"""Sample from the posterior (with gradients).

        For the deterministic posterior, this just returns the values expanded
        to the requested shape.

        Args:
            sample_shape: A `torch.Size` object specifying the sample shape. To
                draw `n` samples, set to `torch.Size([n])`. To draw `b` batches
                of `n` samples each, set to `torch.Size([b, n])`.
            base_samples: An (optional) Tensor of `N(0, I)` base samples of
                appropriate dimension, typically obtained from a `Sampler`.
                Ignored in construction of the samples (used only for shape
                validation).

        Returns:
            A `sample_shape x event`-dim Tensor of samples from the posterior.
        """
        if sample_shape is None:
            sample_shape = torch.Size([1])
        if base_samples is not None:
            if base_samples.shape[: len(sample_shape)] != sample_shape:
                raise RuntimeError("sample_shape disagrees with shape of base_samples.")
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botorch/posteriors/gpytorch.py [65:87]:
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    def rsample(
        self,
        sample_shape: Optional[torch.Size] = None,
        base_samples: Optional[Tensor] = None,
    ) -> Tensor:
        r"""Sample from the posterior (with gradients).

        Args:
            sample_shape: A `torch.Size` object specifying the sample shape. To
                draw `n` samples, set to `torch.Size([n])`. To draw `b` batches
                of `n` samples each, set to `torch.Size([b, n])`.
            base_samples: An (optional) Tensor of `N(0, I)` base samples of
                appropriate dimension, typically obtained from a `Sampler`.
                This is used for deterministic optimization.

        Returns:
            A `sample_shape x event_shape`-dim Tensor of samples from the posterior.
        """
        if sample_shape is None:
            sample_shape = torch.Size([1])
        if base_samples is not None:
            if base_samples.shape[: len(sample_shape)] != sample_shape:
                raise RuntimeError("sample_shape disagrees with shape of base_samples.")
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