+ j=#^RIt^RIHt^RIHt^RIHt^RIHt^RIH t H t ^RI H t ^RI HtR .t!R R ]4tR#) N)Tensor) constraints) Distribution) Independent)ComposeTransform Transform)_sum_rightmost)_sizeTransformedDistributionc,aa]tRt^toRt/tRV3RlV3RllltRV3Rllt]P!RR7R4t ] V3RlR l4t ] P!43R lt] P!43V3R lR lltR tRtRtRtV3RltRtVtV;t#)r aI Extension of the Distribution class, which applies a sequence of Transforms to a base distribution. Let f be the composition of transforms applied:: X ~ BaseDistribution Y = f(X) ~ TransformedDistribution(BaseDistribution, f) log p(Y) = log p(X) + log |det (dX/dY)| Note that the ``.event_shape`` of a :class:`TransformedDistribution` is the maximum shape of its base distribution and its transforms, since transforms can introduce correlations among events. An example for the usage of :class:`TransformedDistribution` would be:: # Building a Logistic Distribution # X ~ Uniform(0, 1) # f = a + b * logit(X) # Y ~ f(X) ~ Logistic(a, b) base_distribution = Uniform(0, 1) transforms = [SigmoidTransform().inv, AffineTransform(loc=a, scale=b)] logistic = TransformedDistribution(base_distribution, transforms) For more examples, please look at the implementations of :class:`~torch.distributions.gumbel.Gumbel`, :class:`~torch.distributions.half_cauchy.HalfCauchy`, :class:`~torch.distributions.half_normal.HalfNormal`, :class:`~torch.distributions.log_normal.LogNormal`, :class:`~torch.distributions.pareto.Pareto`, :class:`~torch.distributions.weibull.Weibull`, :class:`~torch.distributions.relaxed_bernoulli.RelaxedBernoulli` and :class:`~torch.distributions.relaxed_categorical.RelaxedOneHotCategorical` c^<V^8dQhRS[RS[S[S[,,RS[R,RR/#)base_distribution transforms validate_argsNreturn)rrlistbool)format __classdict__s"m/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/distributions/transformed_distribution.py __annotate__$TransformedDistribution.__annotate__4sF:P:P':PY/:Pd{ :P  :Pc<\V\4'd V.VnMp\V\4'dM\;QJdRV4F 'dK RM RM !RV44'g \ R4hW nM\ RV 24hVP VP,p\VP4p\VP4p\V4VPP8d'\ RVPP RV R24hVPV4pVPV4pWH8wd'VR\V4V, p VPV 4pVPPV, p V ^8d \W4pWnVP"PVPP, p \%VP"PW[,4p \V4V 8d\'R \V4 R V 24h\V4V , p VRV pW}Rp\(SV`UWVR 7R#) c3B"TFp\V\4xK R#5iN) isinstancer).0ts& r 3TransformedDistribution.__init__..?sDAz!Y//sFTz6transforms must be a Transform or a list of Transformsz0transforms must be a Transform or list, but was z9base_distribution needs to have shape with size at least z , but got .Nzforward_shape length z must be >= event_dim r)rrrrall ValueError batch_shape event_shapelenrdomain event_dim forward_shape inverse_shapeexpandr base_distcodomainmaxAssertionErrorsuper__init__)selfrrr base_shapebase_event_dim transformr,expanded_base_shapebase_batch_shapereinterpreted_batch_ndimstransform_change_in_event_dimr+cutr'r( __class__s&&&& rr4 TransformedDistribution.__init__4s> j) , ,DO D ) )3DD333DDDD L)OB:,O  '225F5R5RR .::;$T__5 z?Y--77 7KIL\L\LfLfKggqr|q}}~ "// ; '55mD  ,2;#)*^;  !2 8 89I J $-$4$4$>$>$O! $q ( +!! +    ( (9+;+;+E+E E &    ( (  :  }  ) 'M(:';;QR[Q\] - 9,#DS) #D)  Orc<VP\V4p\P!V4pWP,p\ VP 4FpVPV4pK VR\V4\VPP4, pVPPV4Vn VP Vn\\V`3WPRR7VPVn V#)NFr$)_get_checked_instancer torchSizer(reversedrr-r)r/r.r3r4_validate_args)r5r' _instancenewshaper r:r>s&&& rr.TransformedDistribution.expandps(()@)Ljj- ...$//*AOOE*E+ !O3u:DNN4N4N0O#OP--.>?  %s4 )) 5 "00 rF) is_discretecNVP'gVPP#VPR,Pp\ VP 4VP 8d<\P!V\ VP 4VP , 4pV#)) rr/supportr0r)r(r+r independent)r5rNs& rrNTransformedDistribution.supports|>>)) )//"%.. t 7#4#4 4!--T--.1B1BBGrc <V^8dQhRS[/#)rr)r)rrs"rrrs**T*rc.VPP#r)r/ has_rsample)r5s&rrS#TransformedDistribution.has_rsamples~~)))rc \P!4;_uu_4VPPV4pVPF pV!V4pK VuuRRR4# +'giR#;i)z Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched. Samples first from base distribution and applies `transform()` for every transform in the list. N)rBno_gradr/samplerr5 sample_shapexr8s&& rrWTransformedDistribution.samplesI]]__%%l3A!__ aL- ___s 8A!! A2 c&<V^8dQhRS[RS[/#)rrYr)r r)rrs"rrrs  E V rc rVPPV4pVPF pV!V4pK V#)z Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. Samples first from base distribution and applies `transform()` for every transform in the list. )r/rsamplerrXs&& rr^TransformedDistribution.rsamples4 NN " "< 0I! A)rc bVP'dVPV4\VP4pRpTp\ VP 4FpVP V4pW%PPVPP, , pV\VPWd4W%PP, 4, pTpK V\VPPV4V\VPP4, 4,pV#)z Scores the sample by inverting the transform(s) and computing the score using the score of the base distribution and the log abs det jacobian. g)rE_validate_sampler)r(rDrinvr*r+r0r log_abs_det_jacobianr/log_prob)r5valuer+rdyr8rZs&& rrd TransformedDistribution.log_probs      ! !% ((() #& !$//2I a A ))33i6H6H6R6RR RI...q4,,666#HA3n NN # #A & C8R8R4S(S  rc ^pVPFpW#P,pK \V\4'd V^8XdV#W!R, ,R,#)z] This conditionally flips ``value -> 1-value`` to ensure :meth:`cdf` is monotone increasing. g?)rsignrint)r5rerir8s&& r_monotonize_cdf'TransformedDistribution._monotonize_cdfsM I..(D) dC TQYLs{#c))rc VPRRR1,FpVPV4pK VP'dVPP V4VPP V4pVP V4pV#)z Computes the cumulative distribution function by inverting the transform(s) and computing the score of the base distribution. NrM)rrbrEr/racdfrkr5rer8s&& rrnTransformedDistribution.cdfss 2..IMM%(E/     NN + +E 2""5)$$U+ rc VPV4pVPPV4pVPF pV!V4pK V#)z| Computes the inverse cumulative distribution function using transform(s) and computing the score of the base distribution. )rkr/icdfrros&& rrrTransformedDistribution.icdfsE $$U+##E*Ie$E) rcP<V^8dQh/S[S[S[P3,;R&#)rarg_constraints)dictstrr Constraint)rrs"rrrs$D#{5556;Er)r/rr)__name__ __module__ __qualname____firstlineno____doc__rur4r.rdependent_propertyrNpropertyrSrBrCrWr^rdrkrnrr__annotate_func____static_attributes____classdictcell__ __classcell__)r>rs@@rr r sB:rs7 +97F4 % %RlRr