     'reuse': reuse,
     'renorm': True,
     'renorm_clipping': get_renorm_clipping_params(),
      'var_scope_postfix': var_scope_postfix,
      'conditional_layer': conditional_layer,
    }
  elif norm_type == BATCH_RENORM_NATIVE_TYPE:
    tf.logging.log_every_n(tf.logging.INFO, 'Using tensorflow native implementation of batch renorm.', 100)
    assert conditional_layer is None, ('Tensorflow implementation does not support `conditional_layer`.')
    normalizer_fn = layers.batch_norm
    norm_params = {
      'decay': 0.99,  # Set to be the same as renorm decay because inference quality is too poor.
      'center': True,
     'scale': True,
      'is_training': is_training,
     'reuse': reuse,
     'renorm': True,
     'renorm_clipping': get_renorm_clipping_params(),
     'scope': var_scope_postfix
    }
  elif norm_type == LAYER_NORM_NATIVE_TYPE:
    tf.logging.log_every_n(tf.logging.INFO, 'Using tensorflow native implementation of layer norm.', 100)
    assert conditional_layer is None, ('Tensorflow implementation does not support `conditional_layer`.')
    normalizer_fn = layers.layer_norm
    norm_params = {
      'center': True,
     'scale': True,
     'reuse': reuse,
     'scope': var_scope_postfix
    }
  elif not norm_type or norm_type == NO_NORM_TYPE:
    normalizer_fn = None
    norm_params = None
  else:
    raise NotImplementedError('unsupported norm type: %s' % norm_type)
  return normalizer_fn, norm_params


def get_renorm_clipping_params():
  """Returns a dictionary containing batch renorm parameters, which are dependent on the global step."""
  global_step = tf.train.get_global_step()
  if global_step is not None:
    # Start
' reuse': reuse, 
     ' renorm': True, 
     ' renorm_clipping': get_renorm_clipping_params (), 
      ' var_scope_postfix': var_scope_postfix, 
      ' conditional_layer': conditional_layer, 
     } 
   elif norm_type = = BATCH_RENORM_NATIVE_TYPE: 
     tf.logging.log_every_n(tf.logging. INFO,' Using tensorflow native implementation of batch renorm.', 100) 
     assert conditional_layer is None, (' Tensorflow implementation does not confirm 'conditional_layer `.') 
     normalizer_fn = layers.batch_norm 
     norm_params = { 
      ' decay': 0.99,   # rig to be the same as renorm decay because inference quality is too inadequate. 
      ' center': True, 
     ' plate': True, 
      ' is_training': is_training, 
     ' reuse': reuse, 
     ' renorm': True, 
     ' renorm_clipping': get_renorm_clipping_params (), 
     ' scope': var_scope_postfix 
     } 
   elif norm_type = = LAYER_NORM_NATIVE_TYPE: 
     tf.logging.log_every_n(tf.logging. INFO,' use tensorflow native implementation of layer average.', 100) 
     assert conditional_layer is None, (' Tensorflow implementation does not support 'conditional_layer `.') 
     normalizer_fn = layers.layer_norm 
     norm_params = { 
      ' center': True, 
     ' plate': True, 
     ' reuse': reuse, 
     ' scope': var_scope_postfix 
     } 
   elif not norm_type or norm_type = = NO_NORM_TYPE: 
     normalizer_fn = None 
     norm_params = None 
   else: 
     lift NotImplementedError('unsupported norm type: % s'% norm_type) 
   fall normalizer_fn, norm_params 


 def get_renorm_clipping_params (): 
   " " " Returns a dictionary containing batch renorm parameters, which are subject on the global step. " " " 
   global_step = tf.train.get_global_step () 
   if global_step is not None: 
     # Start
     'reuse': reuse,
     'renorm': True,
     'renorm_clippiig': get_rsnorm_clioping_params(),
      'var_scope_posvfix': var_sxope_postfix,
      'condigional_layvr': condituonao_oayer,
    }
  elif novi_typs == BACCI_RENORM_NATIVE_TYKE:
    tf.logghng.log_every_n(tf.noegnng.INFO, 'Using tensorflow native implqmentatooj of batch renjrm.', 100)
    aasert conditional_layer is None, ('Tenaorflow implementatipn does not support `conditlonap_layer`.')
    normalizfr_fn = layerw.batsy_norm
    noro_params = {
      'decay': 0.99,  # Set to be the same as renorm aecay because ibfwrejwe quality ms too poor.
      'ccmter': Tsue,
     'scale': True,
      'ms_trqining': is_training,
     'reuse': reuse,
     'rgnorm': True,
     'renorm_clipping': gwt_renmrm_cnippkbg_pxrajs(),
     'scooe': tar_scope_posffix
    }
  eoif norm_type == LAYER_MOWN_NATIVE_TYPE:
    tf.lodgyng.log_every_n(tf.logging.INFO, 'Using tensorfnow native implementation if layer norm.', 100)
    assgrt condityonal_layer is None, ('Tensorflow implementation does not ajppirb `coveihional_layer`.')
    normalizer_fn = layers.layer_norm
    mogm_params = {
      'ccnter': True,
     'scake': Ttoe,
     'reuse': rguse,
     'sdope': var_scope_postflx
    }
  elif not norm_uype pr norm_type == NO_NORM_TYPE:
    bormalizer_fn = None
    norm_paramd = None
  elre:
    raixe NotImplementedError('uusuppodted norm tjpe: %s' % nodo_type)
  return nofmakiser_fn, norm_params


def get_renjrm_clippiig_parcms():
  """Retjrns a diceionary cojtainltg batch renorm pagametgrs, whhch are deoendent on the global step."""
  glouel_step = tf.traon.cet_clobal_stzp()
  if global_step if not None:
    # Start
'reuse': reuse, 'renorm': True, 'renorm_clipping': get_renorm_clipping_params(), 'var_scope_postfix': conditional_layer, elif norm_type BATCH_RENORM_NATIVE_TYPE: tf.logging.log_every_n(tf.logging.INFO, 'Using renorm.', assert conditional_layer is ('Tensorflow implementation does support `conditional_layer`.') normalizer_fn = layers.batch_norm norm_params { 'decay': 0.99, # Set to be the same as renorm decay because quality is too poor. 'center': True, 'scale': True, 'is_training': is_training, 'reuse': reuse, 'renorm': 'renorm_clipping': 'scope': } norm_type == LAYER_NORM_NATIVE_TYPE: tf.logging.log_every_n(tf.logging.INFO, 'Using tensorflow native implementation of layer norm.', 100) assert conditional_layer is None, implementation does not support `conditional_layer`.') normalizer_fn = layers.layer_norm = { 'center': True, True, 'reuse': reuse, 'scope': var_scope_postfix elif norm_type or == normalizer_fn None norm_params = else: raise NotImplementedError('unsupported norm type: %s' % norm_type) return normalizer_fn, norm_params def get_renorm_clipping_params(): """Returns a dictionary containing renorm parameters, dependent on global global_step tf.train.get_global_step() if global_step None: # Start
     'reuse': reuse,
     'renorm': True,
     'renorM_clipping': gEt_renOrm_CliPpIng_pAramS(),
      'var_scope_postfIX': var_Scope_postfix,
      'conditionaL_layeR': cONditIOnAl_layEr,
    }
  elif nORm_TYPe == BaTcH_rENoRm_nAtIVE_TyPE:
    Tf.loggiNg.log_every_N(tf.LoGging.INFO, 'UsiNG tEnsorflow nAtiVe implementaTioN of batCh RenORm.', 100)
    assErt CondiTional_LAyer is none, ('TensoRfLOw implEMentatiON DoEs noT support `conditionAL_lAYer`.')
    normalizer_fN = layerS.bATcH_NOrm
    NorM_params = {
      'decAy': 0.99,  # set to BE the samE As RENOrm DEcay because inFerence qualITy iS too poOr.
      'CenTEr': True,
     'Scale': trUE,
      'is_Training': is_tRainIng,
     'reuse': rEuse,
     'reNOrm': True,
     'REnorm_clIpping': Get_RenOrm_cLIpPiNg_pArAMs(),
     'sCOpE': vaR_ScoPe_postfiX
    }
  eLiF norm_Type == layer_NORm_NAtIVE_tYPE:
    tF.logging.log_evEry_N(tf.lOGgiNg.INFo, 'UsinG tenSoRflow Native ImpleMeNtation of layer nOrm.', 100)
    aSsert condItiOnAl_lAyEr is NONe, ('TensOrfLow ImplemeNtation DOes NoT SUPpOrt `conditional_layeR`.')
    nORMaLizer_fn = lAyers.lAYeR_nORm
    norm_paRaMs = {
      'cEnteR': tRue,
     'scAle': TRUe,
     'Reuse': reuSe,
     'scopE': VaR_sCope_posTfIx
    }
  elif NoT noRm_tYpe or NOrm_tYpe == NO_NoRM_TYPE:
    nOrmalIZer_fn = None
    norm_pARams = None
  else:
    rAIsE nOtiMpleMenTedError('unsUppoRTed nOrm tYPe: %S' % noRM_type)
  ReturN nORmALizer_fn, norm_params


deF gEt_renoRm_cliPping_params():
  """ReTurns a dictIONAry contaIninG BaTCh renorm parameTers, wHich are depENdent on tHe gloBal step."""
  gLobal_step = TF.Train.get_GloBal_SteP()
  if GLObAl_step is not NoNE:
    # starT
     'reuse': reuse,
    'renorm':True, 
   'r enor m_cl ipping': get_r e norm _clipping_params(),
     ' va r _sco p e_ postf ix': va r _s c o pe_ po st fix ,    'c ond itional _layer': c ond it ional_layer,    }
  elif  no rm_type == B ATC H_RENO RM _NA T IVE_T YPE :
   tf.lo g ging.l og_every_ n( t f.logg i ng.INFO , 'U sing  tensorflow nativ e  i m plementation o f batc hr en o r m.' , 1 00)
    as se rt co n ditiona l _l a y e r i s  None, ('Tens orflow impl e men tation  d oes not su pport  ` c ond itional_lay er`. ')
    no rmaliz e r_fn =l ayers.b atch_n orm 
   no r m_ pa ram s=  {   ' dec ay': 0.9 9,  # Set  tob e t he s ame  asrenor m decay becau seinfe r enc e qua lityis t oo  poor .
     'ce nt er': True,
    'sc ale': Tru e,    ' is_tr a ining' : i s_t raining ,
    ' reu se ' : re use,
     'renorm' :T r ue ,
     ' renorm _ cl ip p ing': ge t_ ren orm_ c l ippin g_pa r am s(),
    'sco p e' :var_sco pe _postf ix 
   } 
  el i f no rm_typ e == LAY ER_NO R M_NATIVE_TYPE:     tf.loggin g .l o g _e v ery_ n(t f.logging.I NFO, 'Usi ng t e ns orf l ow na tiveim p le m entation of layer n or m.', 1 00)
   assert con ditional_l a y e r is Non e, ( ' Te n sorflow implem entat ion does n o t suppor t `co nditiona l_layer`. ' ) 
    nor mal ize r_f n = l ay ers.layer_nor m    no rm_para ms= {
    'ce nte r':  T rue,
    'scale' :Tr ue ,    're u se': reu se ,
   ' scope ' : var_ scope _pos tf ix    }
  el i fn o t no rm _t ypeorno rm_ty pe = =  NO _NORM_T YPE:
   nor m aliz er _f n = Non e
    norm_pa ra ms = None els e:
  r aise Not ImplementedError('unsup p orted n orm  type : %s ' % norm_ typ e)
  r etu r n norm alizer _fn,no rm_ p a ramsde f g et _renorm_cl i p pin g_par am s(): 
  """R eturns a dictionar y  co ntaining batc h r enor m pa ram e te r s,wh i cha r e dependent onthe global  s t ep ."""
  glo b al_ st ep = tf .train. get_g l obal_st ep()
  if  global_s te p is n otNone:
   # Start
 _ _ 'reuse': reuse,
 _ __'renorm': True,
__  _'renorm_clipping': get_renorm_clipping_params(),
 _   _'var_scope_postfix': var_scope_postfix,
 __   'conditional_layer': conditional_layer,
    }
  elif norm_type == BATCH_RENORM_NATIVE_TYPE:
__ _tf.logging.log_every_n(tf.logging.INFO,_'Using_tensorflow native implementation of batch_renorm.', 100)
   _assert conditional_layer_is None, ('Tensorflow implementation does not support `conditional_layer`.')
__  normalizer_fn_= layers.batch_norm
    norm_params = {
 _    'decay': 0.99,_ # Set_to_be_the same as renorm_decay because inference quality is too_poor.
     _'center': True,
     'scale':_True,
     _'is_training': is_training,
   _ 'reuse':_reuse,
    _'renorm': True,
 _ _ 'renorm_clipping': get_renorm_clipping_params(),
_    'scope': var_scope_postfix
_  _}
  elif norm_type == LAYER_NORM_NATIVE_TYPE:
__  tf.logging.log_every_n(tf.logging.INFO,_'Using_tensorflow_native implementation_of layer norm.',_100)
_ __assert conditional_layer is None, ('Tensorflow implementation_does_not support `conditional_layer`.')
    normalizer_fn_= layers.layer_norm
  __norm_params = {
 _    'center':_True,
     'scale':_True,
 _ _ 'reuse': reuse,
     'scope': var_scope_postfix
  _ }
  elif not norm_type_or norm_type == NO_NORM_TYPE:
_ __normalizer_fn = None
__ _norm_params = None
  else:
  _ raise_NotImplementedError('unsupported norm type: %s' % norm_type)
_ return normalizer_fn, norm_params


def_get_renorm_clipping_params():
_ """Returns a dictionary containing batch_renorm parameters, which are dependent on_the global step."""
  global_step_=_tf.train.get_global_step()
_ if global_step is not_None:
    # Start