botorch/models/transforms/input.py [331:365]:
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    ) -> None:
        r"""Normalize the inputs to the unit cube.

        Args:
            d: The dimension of the input space.
            indices: The indices of the inputs to normalize. If omitted,
                take all dimensions of the inputs into account.
            bounds: If provided, use these bounds to normalize the inputs. If
                omitted, learn the bounds in train mode.
            batch_shape: The batch shape of the inputs (asssuming input tensors
                of shape `batch_shape x n x d`). If provided, perform individual
                normalization per batch, otherwise uses a single normalization.
            transform_on_train: A boolean indicating whether to apply the
                transforms in train() mode. Default: True.
            transform_on_eval: A boolean indicating whether to apply the
                transform in eval() mode. Default: True.
            transform_on_fantasize: A boolean indicating whether to apply the
                transform when called from within a `fantasize` call. Default: True.
            reverse: A boolean indicating whether the forward pass should untransform
                the inputs.
            min_range: Amount of noise to add to the range to ensure no division by
                zero errors.
        """
        super().__init__()
        if (indices is not None) and (len(indices) == 0):
            raise ValueError("`indices` list is empty!")
        if (indices is not None) and (len(indices) > 0):
            indices = torch.tensor(indices, dtype=torch.long)
            if len(indices) > d:
                raise ValueError("Can provide at most `d` indices!")
            if (indices > d - 1).any():
                raise ValueError("Elements of `indices` have to be smaller than `d`!")
            if len(indices.unique()) != len(indices):
                raise ValueError("Elements of `indices` tensor must be unique!")
            self.indices = indices
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botorch/models/transforms/input.py [488:518]:
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    ) -> None:
        r"""Standardize inputs (zero mean, unit variance).

        Args:
            d: The dimension of the input space.
            indices: The indices of the inputs to standardize. If omitted,
                take all dimensions of the inputs into account.
            batch_shape: The batch shape of the inputs (asssuming input tensors
                of shape `batch_shape x n x d`). If provided, perform individual
                normalization per batch, otherwise uses a single normalization.
            transform_on_train: A boolean indicating whether to apply the
                transforms in train() mode. Default: True
            transform_on_eval: A boolean indicating whether to apply the
                transform in eval() mode. Default: True
            reverse: A boolean indicating whether the forward pass should untransform
                the inputs.
            min_std: Amount of noise to add to the standard deviation to ensure no
                division by zero errors.
        """
        super().__init__()
        if (indices is not None) and (len(indices) == 0):
            raise ValueError("`indices` list is empty!")
        if (indices is not None) and (len(indices) > 0):
            indices = torch.tensor(indices, dtype=torch.long)
            if len(indices) > d:
                raise ValueError("Can provide at most `d` indices!")
            if (indices > d - 1).any():
                raise ValueError("Elements of `indices` have to be smaller than `d`!")
            if len(indices.unique()) != len(indices):
                raise ValueError("Elements of `indices` tensor must be unique!")
            self.indices = indices
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