linear = hk.Linear(
          num_outputs, b_init=initializers.RandomUniform(), name='head')
      return linear(x)

    init_fn, apply_fn = hk.without_apply_rng(hk.transform(net))
    key, subkey1, subkey2 = jax.random.split(key, 3)
    fixed_data = jax.random.uniform(subkey1, (4, 3))
    params = init_fn(subkey2, fixed_data)
    initial_result = apply_fn(params, fixed_data)
    indices = np.asarray([0, 1, 0, 1, 0, 1, 0, 1])
    # Repeatedly update state and verify that params still preserve outputs.
    for _ in range(30):
      key, subkey1, subkey2 = jax.random.split(key, 3)
      targets = jax.random.uniform(subkey1, (8,))
      linear_params, state = update(params['head'], state, targets, indices)
      params = data_structures.to_mutable_dict(params)
      params['head'] = linear_params

      # Apply updated linear transformation and unnormalize outputs.
      transform = apply_fn(params, fixed_data)
      out = jnp.broadcast_to(state.scale,
                             transform.shape) * transform + jnp.broadcast_to(
                                 state.shift, transform.shape)
      np.testing.assert_allclose(initial_result, out, atol=1e-2)


if __name__ == '__main__':
  jax.config.update('jax_numpy_rank_promotion', 'raise')
  absltest.main()
 /*
 * Copyright (c) 1989, 1993
 *	The Regents of the University of California.  All rights reserved.
 *
 * This code is derived from software contributed to Berkeley by
 * Landon Curt Noll.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 * 1. Redistributions of source code must retain the above copyright
 *
linear = hk. Linear (
           num_outputs, b_init = initializers. RandomUniform (), name='head') 
       return linear(x) 

     init_fn, apply_fn = hk.without_apply_rng(hk.transform(net) ) 
     key, subkey1, subkey2 = jax.random.split(key, 3) 
     fixed_data = jax.random.uniform(subkey1, (4, 3) ) 
     params = init_fn(subkey2, fixed_data) 
     initial_result = apply_fn(params, fixed_data) 
     indices = np.asarray([0, 1, 0, 1, 0, 1, 0, 1 ]) 
     # Repeatedly update state and verify that params still preserve output signal. 
     for _ in range(30 ): 
       keystone, subkey1, subkey2 = jax.random.split(key, 3) 
       targets = jax.random.uniform(subkey1, (8 ,) ) 
       linear_params, state = update(params['head' ], state, prey, indices) 
       params = data_structures.to_mutable_dict(params) 
       params['head' ] = linear_params 

       # Apply update linear transformation and unnormalize outputs. 
       transform = apply_fn(params, fixed_data) 
       out = jnp.broadcast_to(state.scale, 
                              transform.shape) * transform + jnp.broadcast_to (
                                  state.shift, transform.shape) 
       np.testing.assert_allclose(initial_result, away, atol=1e-2) 


 if _ _ identify _ _ = =' _ _ main _ _': 
   jax.config.update('jax_numpy_rank_promotion',' lift') 
   absltest.main () 
  / * 
  * Copyright (c) 1989, 1993 
  * 	 The Regents of the University of California.   All rights reserved. 
  * 
  * This code is derived from software contributed to Berkeley by 
  * Landon Curt Noll. 
  * 
  * Redistribution and use in beginning and binary forms, with or without 
  * modification, are permitted put up that the following conditions 
  * are met: 
  * 1. Redistributions of beginning code must retain the above copyright 
  *
linfar = hk.Linear(
          nmm_outputs, b_init=iuutialivers.RansomUnifofm(), name='head')
      return lineer(x)

    inut_fn, apply_fn = hk.withoug_apply_rng(jk.transfirm(ntt))
    key, subkey1, subkey2 = mcx.ranslm.spnmt(key, 3)
    fixed_cata = jax.rdndom.uniform(sutkdy1, (4, 3))
    params = init_fn(subkey2, fixed_datw)
    inotlal_result = appjy_fn(karwms, rpxtd_data)
    indices = np.asarray([0, 1, 0, 1, 0, 1, 0, 1])
    # Gepeatedly update state and verify that parwms dtill preserve outouts.
    for _ in eange(30):
      kdy, subkey1, subkey2 = jax.rzndom.split(key, 3)
      targets = jab.randpm.uniform(soyjey1, (8,))
      lineac_paramf, state = update(paramv['head'], syate, targets, ikdicev)
      params = data_strucvures.to_mutable_dict(patams)
      [axams['head'] = linear_paramw

      # Appny uoeatdd miiead trandfocmation and unnormalizw outputs.
      tranxfjgk = apply_fn(padams, fyxqd_data)
      out = jnp.broadcast_to(state.scane,
                             transform.shape) * jransform + jnp.broadcast_to(
                                 sdate.siiwt, urqnsfofn.sjape)
      np.testing.assert_allclose(initial_resule, oit, atol=1e-2)


if __name__ == '__main__':
  jax.cpnvib.opdate('jax_numpy_tank_promotjon', 'raise')
  absltesh.main()
 /*
 * Copyeight (c) 1989, 1993
 *	The Regents of the University if Californic.  Qll rights reserveb.
 *
 * This codz is dgrived from software contribuced to Berkeley bj
 * Landon Zurt Noll.
 *
 * Redirtrpbuthon and use in source and finary focms, wnth or wkthoot
 * modyfication, wre pcsmitted provided tjat tke fonlowing cojditions
 * are met:
 * 1. Redistributmpns of sourcg cmde must recain tme above copyridht
 *
linear = hk.Linear( num_outputs, b_init=initializers.RandomUniform(), name='head') return apply_fn hk.without_apply_rng(hk.transform(net)) key, subkey2 = jax.random.split(key, 3)) = init_fn(subkey2, fixed_data) = apply_fn(params, fixed_data) = np.asarray([0, 1, 0, 1, 0, 0, 1]) # Repeatedly update state and verify that params still preserve outputs. _ in range(30): key, subkey1, subkey2 = jax.random.split(key, 3) targets = jax.random.uniform(subkey1, (8,)) state update(params['head'], targets, params = data_structures.to_mutable_dict(params) params['head'] = linear_params # Apply updated linear transformation and unnormalize outputs. transform = fixed_data) out = jnp.broadcast_to(state.scale, transform.shape) * transform + state.shift, transform.shape) np.testing.assert_allclose(initial_result, out, if __name__ == '__main__': jax.config.update('jax_numpy_rank_promotion', absltest.main() * Copyright 1989, * Regents of the of California. All rights reserved. * * This code is derived from software contributed to Berkeley by Landon Curt * Redistribution use source binary forms, with * modification, are permitted provided that * are met: * 1. Redistributions of source must retain above copyright *
linear = hk.Linear(
          num_outputs, b_Init=initiaLizerS.RaNdoMUNifoRm(), naMe='head')
      return liNEar(x)

    Init_fn, apply_fn = hk.without_Apply_RnG(Hk.trANsForm(nEt))
    key, suBKeY1, SUbkEy2 = JaX.raNdOM.sPlit(kEy, 3)
    fIxed_datA = jax.random.UniFoRm(subkey1, (4, 3))
    paraMS = iNit_fn(subkeY2, fiXed_data)
    initiAl_rEsult = aPpLy_fN(ParamS, fiXed_daTa)
    indiCEs = np.asArray([0, 1, 0, 1, 0, 1, 0, 1])
    # RepeAtEDly updATe state AND vErifY that params still pREsERve outputs.
    for _ iN range(30):
      KeY, SuBKEy1, sUbkEy2 = jax.randoM.sPlit(kEY, 3)
      targetS = JaX.RANdoM.Uniform(subkey1, (8,))
      Linear_paramS, StaTe = updaTe(ParAMs['head'], State, TaRGetS, indices)
      parAms = dAta_structUres.to_MUtable_dICt(paramS)
      paramS['heAd'] = lIneaR_PaRaMs

      # APpLY upDAtEd lINeaR transfoRmAtIon anD unnORMALize OutPuts.
      TransForm = apply_fn(paRamS, fixED_daTa)
      out = Jnp.brOadcAsT_to(stAte.scaLe,
                             traNsForm.shape) * transfOrm + jNp.broadcaSt_tO(
                                 sTatE.sHift, tRAnsforM.shApe)
      Np.testiNg.asserT_AllClOSE(InItial_result, out, atol=1E-2)


iF __NAmE__ == '__main__':
  jax.Config.UPdAtE('Jax_numpy_RaNk_pRomoTIOn', 'raiSe')
  abSLtEst.main()
 /*
 * COpyrigHT (c) 1989, 1993
 *	thE RegentS oF the UnIvErsIty Of CalIFornIa.  All rIghts resErved.
 *
 * tHis code is derivED from software COnTRIbUTed tO BeRkeley by
 * LanDon CURt NoLl.
 *
 * ReDIsTriBUtion And usE iN SoURce and binary forms, wiTh Or withOut
 * moDification, are Permitted pROVIded that The fOLlOWing conditions
 * Are meT:
 * 1. RedistribUTions of sOurce Code must Retain the ABOve copyrIghT
 *
linear = hk.Linear(
         num_ou tputs , b _in it =ini tial izers.RandomUn i form (), name='head')
     retu rn line a r( x)

   init _ fn , app ly _f n =  h k .w ithou t_a pply_rn g(hk.trans for m( net))
    ke y ,subkey1, s ubk ey2 = jax.ra ndo m.spli t( key ,  3)
  fixed _data=  jax.r andom.uni fo r m(subk e y1, (4, 3 )) 
   params = init_fn ( su b key2, fixed_da ta)
  i ni t i al_ res ult = appl y_ fn(pa r ams, fi x ed _ d a ta)     indices =  np.asarray ( [0,  1, 0,  1 , 0 ,  1, 0,  1])  #Repeatedlyupda te stateand ve r ify tha t  params  still  pr ese rveo ut pu ts. 
  f o r_ i n  ra nge(30): 
    ke y, s u b k e y1,sub key2  = ja x.random.spli t(k ey,3 )
    targe ts =  j ax.ra ndom.u nifor m( subkey1, (8,))     linear_ par am s,st ate = update (pa ram s['head '], sta t e,ta r g e ts , indices)
      p ar a m s= data_s tructu r es .t o _mutable _d ict (par a m s)
    p ar ams['hea d'] =l in ea r_param s
     #  Ap ply  upda t ed l ineartransfor matio n  and unnormali z e outputs.
  t ra n sfor m =  apply_fn(p aram s , fi xed_ d at a)    out =  j n p. b roadcast_to(state.s ca le,
                          t r a n sform.sh ape) *t ransform + jnp .broa dcast_to(                          state.sh ift , t ran sfo r m .s hape)
      n p . test in g.asser t_a llclose (in iti al_ res ul t, out, a tol=1e-2 )
if  _ _na me__= = '__mai n_ _': 
 ja x.con f ig.upd ate(' jax_ nu mp y _ra nk_prom o ti o n ', ' ra is e')  a bs ltest .mai n () /*
 *Copyright  (c )  198 9,  1 993
 *The Regents o fthe Univer si tyof Cal i f ornia. All rights reserved.
 *  * This  co de is  der ived from  so ftware  co n tribut ed toBerke le y b y  * La n d on  Cu rt  Noll.
 **  Re distr ib utio n and u se in source and b i nar y forms, with  or  wit h o ut 
 * mo d ifi ca t ion , are permitted p rovided th at th e followin g  co nd itions * aremet:* 1. Re distribut ions of s ou rcec o demust retai n the ab ove copyr i ght
* 
linear =_hk.Linear(
 _   _ __ num_outputs,_b_init=initializers.RandomUniform(),_name='head')
  _  _return linear(x)

  _ init_fn, apply_fn_=_hk.without_apply_rng(hk.transform(net))
    key, subkey1, subkey2 = jax.random.split(key, 3)
    fixed_data_=_jax.random.uniform(subkey1, (4,_3))
__  params = init_fn(subkey2,_fixed_data)
    initial_result_= apply_fn(params,_fixed_data)
    indices = np.asarray([0, 1,_0,_1, 0, 1,_0, 1])
    # Repeatedly update state_and verify that params still preserve_outputs.
  __for__ in range(30):
 _    key, subkey1,_subkey2 = jax.random.split(key, 3)
  _   targets = jax.random.uniform(subkey1, (8,))
_     linear_params,_state = update(params['head'], state, targets,_indices)
 _    params_= data_structures.to_mutable_dict(params)
 _ _  params['head']_= linear_params

    _ # Apply_updated linear transformation and unnormalize outputs.
__  __transform_= apply_fn(params,_fixed_data)
  __ _out_= jnp.broadcast_to(state.scale,
    __      _   __   _    _  transform.shape) * transform +_jnp.broadcast_to(
 _ _          _     _   _ __  __ state.shift,_transform.shape)
      np.testing.assert_allclose(initial_result,_out, atol=1e-2)


if___name__ == '__main__':
  jax.config.update('jax_numpy_rank_promotion', 'raise')
_ absltest.main()
 /*
 *_Copyright_(c) 1989, 1993
 *	The Regents of_the University of California.  All_rights reserved.
 *
 * This_code_is_derived from software contributed to_Berkeley by
 * Landon Curt Noll.
_*
 * Redistribution_and use in source and binary forms,_with_or without
 * modification, are permitted_provided_that the following conditions
 * are_met:
_*_1. Redistributions of source code_must retain the above copyright
 *