flowtorch/bijectors/affine.py [12:30]:
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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,
        log_scale_min_clip: float = -5.0,
        log_scale_max_clip: float = 3.0,
        sigmoid_bias: float = 2.0,
    ) -> None:
        super().__init__(params_fn, shape=shape, context_shape=context_shape)
        self.log_scale_min_clip = log_scale_min_clip
        self.log_scale_max_clip = log_scale_max_clip
        self.sigmoid_bias = sigmoid_bias
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flowtorch/bijectors/ops/affine.py [13:32]:
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    r"""
    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,
        log_scale_min_clip: float = -5.0,
        log_scale_max_clip: float = 3.0,
        sigmoid_bias: float = 2.0,
    ) -> None:
        super().__init__(params_fn, shape=shape, context_shape=context_shape)
        self.log_scale_min_clip = log_scale_min_clip
        self.log_scale_max_clip = log_scale_max_clip
        self.sigmoid_bias = sigmoid_bias
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