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# Module: tfp.bijectors

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Bijective transformations.

## Classes

`class AbsoluteValue`: Computes `Y = g(X) = Abs(X)`, element-wise.

`class Ascending`: Maps unconstrained R^n to R^n in ascending order.

`class AutoCompositeTensorBijector`: Base for `CompositeTensor` bijectors with auto-generated `TypeSpec`s.

`class AutoregressiveNetwork`: Masked Autoencoder for Distribution Estimation [Germain et al. (2015)][1].

`class BatchNormalization`: Compute `Y = g(X) s.t. X = g^-1(Y) = (Y - mean(Y)) / std(Y)`.

`class Bijector`: Interface for transformations of a `Distribution` sample.

`class Blockwise`: Bijector which applies a list of bijectors to blocks of a `Tensor`.

`class Chain`: Bijector which applies a composition of bijectors.

`class CholeskyOuterProduct`: Compute `g(X) = X @ X.T`; X is lower-triangular, positive-diagonal matrix.

`class CholeskyToInvCholesky`: Maps the Cholesky factor of `M` to the Cholesky factor of `M^{-1}`.

`class Composition`: Base class for Composition bijectors (Chain, JointMap).

`class CorrelationCholesky`: Maps unconstrained reals to Cholesky-space correlation matrices.

`class Cumsum`: Computes the cumulative sum of a tensor along a specified axis.

`class DiscreteCosineTransform`: Compute `Y = g(X) = DCT(X)`, where DCT type is indicated by the `type` arg.

`class Exp`: Compute `Y = g(X) = exp(X)`.

`class Expm1`: Compute `Y = g(X) = exp(X) - 1`.

`class FFJORD`: Implements a continuous normalizing flow X->Y defined via an ODE.

`class FillScaleTriL`: Transforms unconstrained vectors to TriL matrices with positive diagonal.

`class FillTriangular`: Transforms vectors to triangular.

`class FrechetCDF`: The Frechet cumulative density function.

`class GeneralizedExtremeValueCDF`: Compute the GeneralizedExtremeValue CDF.

`class GeneralizedPareto`: Bijector mapping R**n to non-negative reals.

`class Glow`: Implements the Glow Bijector from Kingma & Dhariwal (2018)[1].

`class GlowDefaultExitNetwork`: Default network for the glow exit bijector.

`class GlowDefaultNetwork`: Default network for the glow bijector.

`class GompertzCDF`: Compute `Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1)`, the Gompertz CDF.

`class GumbelCDF`: Compute `Y = g(X) = exp(-exp(-(X - loc) / scale))`, the Gumbel CDF.

`class Householder`: Compute the reflection of a vector across a hyperplane.

`class Identity`: Compute Y = g(X) = X.

`class Inline`: Bijector constructed from custom callables.

`class Invert`: Bijector which inverts another Bijector.

`class IteratedSigmoidCentered`: Bijector which applies a Stick Breaking procedure.

`class JointMap`: Bijector which applies a structure of bijectors in parallel.

`class KumaraswamyCDF`: Compute `Y = g(X) = (1 - X**a)**b, X in [0, 1]`.

`class LambertWTail`: LambertWTail transformation for heavy-tail Lambert W x F random variables.

`class Log`: Compute `Y = log(X)`. This is `Invert(Exp())`.

`class Log1p`: Compute `Y = log1p(X)`. This is `Invert(Expm1())`.

`class MaskedAutoregressiveFlow`: Affine MaskedAutoregressiveFlow bijector.

`class MatrixInverseTriL`: Computes `g(L) = inv(L)`, where `L` is a lower-triangular matrix.

`class MatvecLU`: Matrix-vector multiply using LU decomposition.

`class MoyalCDF`: Compute `Y = g(X) = erfc(exp(- 1/2 * (X - loc) / scale) / sqrt(2))`.

`class NormalCDF`: Compute `Y = g(X) = NormalCDF(x)`.

`class Pad`: Pads a value to the `event_shape` of a `Tensor`.

`class Permute`: Permutes the rightmost dimension of a `Tensor`.

`class Power`: Compute `g(X) = X ** power`; where X is a non-negative real number.

`class PowerTransform`: Compute `Y = g(X) = (1 + X * c)**(1 / c), X >= -1 / c`.

`class RationalQuadraticSpline`: A piecewise rational quadratic spline, as developed in [1].

`class RayleighCDF`: Compute `Y = g(X) = 1 - exp( -(X/scale)**2 / 2 ), X >= 0`.

`class RealNVP`: RealNVP 'affine coupling layer' for vector-valued events.

`class Reciprocal`: A `Bijector` that computes the reciprocal `b(x) = 1. / x` entrywise.

`class Reshape`: Reshapes the `event_shape` of a `Tensor`.

`class Restructure`: Converts between nested structures of Tensors.

`class Scale`: Compute `Y = g(X; scale) = scale * X`.

`class ScaleMatvecDiag`: Compute `Y = g(X; scale) = scale @ X`.

`class ScaleMatvecLU`: Matrix-vector multiply using LU decomposition.

`class ScaleMatvecLinearOperator`: Compute `Y = g(X; scale) = scale @ X`.

`class ScaleMatvecLinearOperatorBlock`: Compute `Y = g(X; scale) = scale @ X` for blockwise `X` and `scale`.

`class ScaleMatvecTriL`: Compute `Y = g(X; scale) = scale @ X`.

`class Shift`: Compute `Y = g(X; shift) = X + shift`.

`class ShiftedGompertzCDF`: Compute `Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate * X))`.

`class Sigmoid`: Bijector that computes the logistic sigmoid function.

`class Sinh`: Bijector that computes `Y = sinh(X)`.

`class SinhArcsinh`: `Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight ) * multiplier`.

`class SoftClip`: Bijector that approximates clipping as a continuous, differentiable map.

`class Softfloor`: Compute a differentiable approximation to `tf.math.floor`.

`class SoftmaxCentered`: Bijector which computes `Y = g(X) = exp([X 0]) / sum(exp([X 0]))`.

`class Softplus`: Bijector which computes `Y = g(X) = Log[1 + exp(X)]`.

`class Softsign`: Bijector which computes `Y = g(X) = X / (1 + |X|)`.

`class Split`: Split a `Tensor` event along an axis into a list of `Tensor`s.

`class Square`: Compute `g(X) = X^2`; X is a positive real number.

`class Tanh`: Bijector that computes `Y = tanh(X)`, therefore `Y in (-1, 1)`.

`class TransformDiagonal`: Applies a Bijector to the diagonal of a matrix.

`class Transpose`: Compute `Y = g(X) = transpose_rightmost_dims(X, rightmost_perm)`.

`class WeibullCDF`: Compute `Y = g(X) = 1 - exp( -( X / scale) ** concentration), X >= 0`.

## Functions

`masked_autoregressive_default_template(...)`: Build the Masked Autoregressive Density Estimator (Germain et al., 2015). (deprecated)

`masked_dense(...)`: A autoregressively masked dense layer. Analogous to `tf.layers.dense`.

`pack_sequence_as(...)`: Returns a Bijector variant of tf.nest.pack_sequence_as.

`real_nvp_default_template(...)`: Build a scale-and-shift function using a multi-layer neural network.

`tree_flatten(...)`: Returns a Bijector variant of tf.nest.flatten.

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