flowtorch/bijectors/affine.py [12:22]:
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    r"""
    Elementwise bijector via the affine mapping :math:`\mathbf{y} = \mu +
    \sigma \otimes \mathbf{x}` where $\mu$ and $\sigma$ are learnable parameters.
    """

    def __init__(
        self,
        params_fn: Optional[flowtorch.Lazy] = None,
        *,
        shape: torch.Size,
        context_shape: Optional[torch.Size] = None,
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flowtorch/bijectors/affine_fixed.py [13:25]:
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    r"""
    Elementwise bijector via the affine mapping :math:`\mathbf{y} = \mu +
    \sigma \otimes \mathbf{x}` where $\mu$ and $\sigma$ are fixed rather than
    learnable.
    """

    # TODO: Handle non-scalar loc and scale with correct broadcasting semantics
    def __init__(
        self,
        params_fn: Optional[flowtorch.Lazy] = None,
        *,
        shape: torch.Size,
        context_shape: Optional[torch.Size] = None,
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