botorch/sampling/samplers.py [149:211]:
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    r"""Sampler for MC base samples using iid N(0,1) samples.

    Example:
        >>> sampler = IIDNormalSampler(1000, seed=1234)
        >>> posterior = model.posterior(test_X)
        >>> samples = sampler(posterior)
    """

    def __init__(
        self,
        num_samples: int,
        resample: bool = False,
        seed: Optional[int] = None,
        collapse_batch_dims: bool = True,
        batch_range: Tuple[int, int] = (0, -2),
    ) -> None:
        r"""Sampler for MC base samples using iid `N(0,1)` samples.

        Args:
            num_samples: The number of samples to use.
            resample: If `True`, re-draw samples in each `forward` evaluation -
                this results in stochastic acquisition functions (and thus should
                not be used with deterministic optimization algorithms).
            seed: The seed for the RNG. If omitted, use a random seed.
            collapse_batch_dims: If True, collapse the t-batch dimensions to
                size 1. This is useful for preventing sampling variance across
                t-batches.
            batch_range: The range of t-batch dimensions in the `base_sample_shape`
                used by `collapse_batch_dims`. The t-batch dims are
                batch_range[0]:batch_range[1]. By default, this is (0, -2),
                for the case where the non-batch dimensions are -2 (q) and
                -1 (d) and all dims in the front are t-batch dims.
        """
        super().__init__(batch_range=batch_range)
        self._sample_shape = torch.Size([num_samples])
        self.collapse_batch_dims = collapse_batch_dims
        self.resample = resample
        self.seed = seed if seed is not None else torch.randint(0, 1000000, (1,)).item()

    def _construct_base_samples(self, posterior: Posterior, shape: torch.Size) -> None:
        r"""Generate iid `N(0,1)` base samples (if necessary).

        This function will generate a new set of base samples and set the
        `base_samples` buffer if one of the following is true:

        - `resample=True`
        - the MCSampler has no `base_samples` attribute.
        - `shape` is different than `self.base_samples.shape` (if
            `collapse_batch_dims=True`, then batch dimensions of will be
            automatically broadcasted as necessary). This shape is expected to
            be `sample_shape + base_sample_shape`, where `base_sample_shape` has been
            adjusted to account for `collapse_batch_dims` (i.e., the output
            of the function `_get_base_sample_shape`).

        Args:
            posterior: The Posterior for which to generate base samples.
            shape: The shape of the base samples to construct.
        """
        if (
            self.resample
            or _check_shape_changed(self.base_samples, self.batch_range, shape)
            or (not self.collapse_batch_dims and shape != self.base_samples.shape)
        ):
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botorch/sampling/samplers.py [227:290]:
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    r"""Sampler for quasi-MC base samples using Sobol sequences.

    Example:
        >>> sampler = SobolQMCNormalSampler(1024, seed=1234)
        >>> posterior = model.posterior(test_X)
        >>> samples = sampler(posterior)
    """

    def __init__(
        self,
        num_samples: int,
        resample: bool = False,
        seed: Optional[int] = None,
        collapse_batch_dims: bool = True,
        batch_range: Tuple[int, int] = (0, -2),
    ) -> None:
        r"""Sampler for quasi-MC base samples using Sobol sequences.

        Args:
            num_samples: The number of samples to use. As a best practice,
                use powers of 2.
            resample: If `True`, re-draw samples in each `forward` evaluation -
                this results in stochastic acquisition functions (and thus should
                not be used with deterministic optimization algorithms).
            seed: The seed for the RNG. If omitted, use a random seed.
            collapse_batch_dims: If True, collapse the t-batch dimensions to
                size 1. This is useful for preventing sampling variance across
                t-batches.
            batch_range: The range of t-batch dimensions in the `base_sample_shape`
                used by `collapse_batch_dims`. The t-batch dims are
                batch_range[0]:batch_range[1]. By default, this is (0, -2),
                for the case where the non-batch dimensions are -2 (q) and
                -1 (d) and all dims in the front are t-batch dims.
        """
        super().__init__(batch_range=batch_range)
        self._sample_shape = torch.Size([num_samples])
        self.collapse_batch_dims = collapse_batch_dims
        self.resample = resample
        self.seed = seed if seed is not None else torch.randint(0, 1000000, (1,)).item()

    def _construct_base_samples(self, posterior: Posterior, shape: torch.Size) -> None:
        r"""Generate quasi-random Normal base samples (if necessary).

        This function will generate a new set of base samples and set the
        `base_samples` buffer if one of the following is true:

        - `resample=True`
        - the MCSampler has no `base_samples` attribute.
        - `shape` is different than `self.base_samples.shape` (if
            `collapse_batch_dims=True`, then batch dimensions of will be
            automatically broadcasted as necessary). This shape is expected to
            be `sample_shape + base_sample_shape`, where `base_sample_shape` has been
            adjusted to account for `collapse_batch_dims` (i.e., the output
            of the function `_get_base_sample_shape`).

        Args:
            posterior: The Posterior for which to generate base samples.
            shape: The shape of the base samples to construct.
        """
        if (
            self.resample
            or _check_shape_changed(self.base_samples, self.batch_range, shape)
            or (not self.collapse_batch_dims and shape != self.base_samples.shape)
        ):
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