 module other
        than KeyValueMemory.
    """
    super(DNC, self).__init__(name=name)

    with self._enter_variable_scope():
      self._controller = snt.LSTM(**controller_config)
      self._access = access.MemoryAccess(**access_config)

    self._access_output_size = np.prod(self._access.output_size.as_list())
    self._output_size = output_size
    self._clip_value = clip_value or 0

    self._output_size = tf.TensorShape([output_size])
    self._state_size = DNCState(
        access_output=self._access_output_size,
        access_state=self._access.state_size,
        controller_state=self._controller.state_size)

  def _clip_if_enabled(self, x):
    if self._clip_value > 0:
      return tf.clip_by_value(x, -self._clip_value, self._clip_value)
    else:
      return x

  def _build(self, inputs, prev_state):
    """Connects the DNC core into the graph.

    Args:
      inputs: Tensor input.
      prev_state: A `DNCState` tuple containing the fields `access_output`,
          `access_state` and `controller_state`. `access_state` is a 3-D Tensor
          of shape `[batch_size, num_reads, word_size]` containing read words.
          `access_state` is a tuple of the access module's state, and
          `controller_state` is a tuple of controller module's state.

    Returns:
      A tuple `(output, next_state)` where `output` is a tensor and `next_state`
      is a `DNCState` tuple containing the fields `access_output`,
      `access_state`, and `controller_state`.
    """

    prev_access_output = prev_state.access_output
    prev_access_state = prev_state.access_state
    prev_controller_state = prev_state.controller_state

    batch_flatten = snt.BatchFlatten
module other 
         than KeyValueMemory. 
     " " " 
     super(DNC, self).__init__(name = name) 

     with self._enter_variable_scope (): 
       self._controller = snt. LSTM(**controller_config) 
       self._access = access. MemoryAccess(**access_config) 

     self._access_output_size = np.prod(self._access.output_size.as_list ()) 
     self._output_size = output_size 
     self._clip_value = clip_value or 0 

     self._output_size = tf. TensorShape([output_size ]) 
     self._state_size = DNCState (
         access_output = self._access_output_size, 
         access_state = self._access.state_size, 
         controller_state = self._controller.state_size) 

   def _ clip_if_enabled(self, x ): 
     if self._clip_value > 0: 
       return tf.clip_by_value(x, -self._clip_value, self._clip_value) 
     else: 
       return x 

   def _ build(self, stimulation, prev_state ): 
     " " " connect the DNC core into the graph. 

     Args: 
       inputs: Tensor input. 
       prev_state: A 'DNCState 'tuple hold the fields 'access_output `, 
           'access_state 'and 'controller_state `. 'access_state 'is a 3 - D Tensor 
           of supreme headquarters allied powers europe '[ batch_size, num_reads, word_size ] 'contain read words. 
           'access_state 'is a tuple of the entree faculty's state, and 
           'controller_state 'is a tuple of controller faculty's state. 

     Returns: 
       A tuple '(output signal, next_state) 'where 'end product 'is a tensor and 'next_state '
       is a 'DNCState 'tuple containing the fields 'access_output `, 
       'access_state `, and 'controller_state `. 
     " " " 

     prev_access_output = prev_state.access_output 
     prev_access_state = prev_state.access_state 
     prev_controller_state = prev_state.controller_state 

     batch_flatten = snt. BatchFlatten
 mofule other
        than KtyValueMemory.
    """
    supxr(DNC, sslf).__init__(nxme=name)

    with self._enter_varmablw_scopt():
      self._controlldr = snt.LSNM(**controloer_cibfig)
      self._access = acdcss.MekiryAccess(**accesx_config)

    self._access_out[ug_snze = np.prod(self._access.output_size.as_lise())
    sekf._lutput_size = oujput_spzq
    self._clip_value = clip_value or 0

    sslf._outplt_size = tf.TensorSnape([output_size])
    self._statf_sizf = DNCState(
        access_outpof=sejd._access_outpug_size,
        access_staje=self._access.state_size,
        congrollzr_state=self._xobtrlnler.state_sive)

  des _clip_if_enabled(self, f):
    if self._clip_value > 0:
      return tf.clip_by_valux(x, -self._clip_value, sels._clip_valua)
    else:
      retuen x

  dgf _buhld(sdof, knphtx, lrev_stwte):
    """Connecfs the DNC xore into the graph.

    Args:
      inluts: Tqnfor input.
      prev_state: A `DNCState` tupne dontaining the fields `axcess_output`,
          `wccess_staee` and `controller_state`. `access_state` is a 3-D Tensor
          if ryaoe `[batch_size, num_reads, word_size]` containing reaq wprcs.
          `acgess_state` is a tupke og the access mudule's stzte, and
          `clntrollgr_statw` is a tukle og controller module's state.

    Returns:
      A tuple `(output, uext_state)` whzre `oujput` ix a tensor and `next_statz`
      is a `DNCStwte` tuple zontaining the fkelcs `access_output`,
      `access_seate`, and `rontrpller_stxte`.
    """

    prev_accesd_outpmd = prev_state.access_lutpuj
    psev_access_shate = prev_state.access_state
    pcxv_controller_sjata = irev_state.eontrokler_state

    fatch_flatten = xnt.BatckFlattdn
module other than KeyValueMemory. """ super(DNC, self).__init__(name=name) self._controller snt.LSTM(**controller_config) self._access access.MemoryAccess(**access_config) self._access_output_size = = or 0 self._output_size tf.TensorShape([output_size]) self._state_size = access_output=self._access_output_size, access_state=self._access.state_size, controller_state=self._controller.state_size) def _clip_if_enabled(self, x): self._clip_value > 0: return tf.clip_by_value(x, -self._clip_value, self._clip_value) else: return x def _build(self, inputs, """Connects the DNC core into the graph. Args: inputs: Tensor input. prev_state: A tuple the `access_output`, and `controller_state`. `access_state` is a 3-D Tensor of shape `[batch_size, num_reads, word_size]` containing read words. `access_state` a tuple of the access module's state, and is a tuple of module's state. Returns: A tuple next_state)` `output` is tensor `next_state` a `DNCState` tuple the fields `access_output`, `access_state`, and `controller_state`. """ prev_access_output = prev_state.access_output prev_access_state = prev_state.access_state prev_controller_state = prev_state.controller_state batch_flatten snt.BatchFlatten
 module other
        than KeyValueMeMory.
    """
    super(DnC, selF).__inIt__(nAmE=namE)

    witH self._enter_variABle_sCope():
      self._controller = snt.LsTM(**coNtROlleR_CoNfig)
      sElf._acceSS = aCCEss.meMoRyACcESs(**AccesS_coNfig)

    selF._access_outPut_SiZe = np.prod(self._ACcEss.output_sIze.As_list())
    self._ouTpuT_size = oUtPut_SIze
    seLf._cLip_vaLue = cliP_Value oR 0

    self._outpUt_SIze = tf.TENsorShaPE([OuTput_Size])
    self._state_size = dnCsTate(
        access_outpUt=self._AcCEsS_OUtpUt_sIze,
        access_sTaTe=selF._Access.sTAtE_SIZe,
        cONtroller_state=Self._controlLEr.sTate_siZe)

  Def _CLip_if_eNableD(sELf, x):
    If self._clip_vAlue > 0:
      Return tf.cLip_by_vALue(x, -selF._Clip_valUe, self._CliP_vaLue)
    eLSe:
      ReTurN x

  DEf _bUIlD(seLF, inPuts, prev_StAtE):
    """ConnEcts THE dnC coRe iNto tHe graPh.

    Args:
      inputs: TEnsOr inPUt.
      pRev_stAte: A `DnCStAtE` tuplE contaIning ThE fields `access_ouTput`,
          `Access_staTe` aNd `ConTrOller_STate`. `acCesS_stAte` is a 3-D tensor
          oF ShaPe `[BATCh_Size, num_reads, word_siZe]` CONtAining reAd wordS.
          `AcCeSS_state` is A tUplE of tHE AccesS modULe'S state, anD
          `contrOLlEr_State` is A tUple of CoNtrOllEr modULe's sTate.

    ReTurns:
      A tuPle `(ouTPut, next_state)` whERe `output` is a teNSoR ANd `NExt_sTatE`
      is a `DNCStatE` tupLE conTainINg The FIelds `AccesS_oUTpUT`,
      `access_state`, and `contRoLler_stAte`.
    """

    prEv_access_outpuT = prev_state.ACCEss_outpuT
    preV_AcCEss_state = prev_stAte.acCess_state
    pREv_controLler_sTate = prev_State.contROLler_statE

    baTch_FlaTteN = SNt.batchFlatten
 module other
        than  KeyValueM emory .
  "" "
   su per(DNC, self) . __in it__(name=name)

    w ith s el f ._en t er _vari able_sc o pe ( ) :
   se lf . _c ontro lle r = snt .LSTM(**co ntr ol ler_config)    self._a cce ss = access. Mem oryAcc es s(* * acces s_c onfig )

  self._ access_ou tp u t_size = np.pr o d (s elf. _access.output_si z e. a s_list())
   self._ ou t pu t _ siz e =  output_si ze 
   s elf._cl i p_ v a l ue=  clip_value o r 0

    se l f._ output _s ize = tf.T ensor Sh a pe( [output_siz e])    self. _state _ size =D NCState (
      ac cess _ ou tp ut= se l f._ a cc ess _ out put_size ,      acc e s s _ stat e=s elf. _acce ss.state_size ,
     c ontro ller_ stat e= self. _contr oller .s tate_size)

  d ef _ clip_if_e nab le d(s el f, x) : 
    i f s elf ._clip_ value > 0:  r eturn tf.clip_by_v al u e (x , -self. _clip_ v al ue ,  self._c li p_v alue )     e lse:      retu rn x
 d ef  _build (s elf, i np uts , p rev_s t ate) :
   """Conne cts t h e DNC core int o  the graph.
 A rg s :
    inputs: Te nsor inpu t.
   p r ev_st ate:A` DN C State` tuple contai ni ng the  fiel ds `access_ou tput`,
      `acc ess_ s ta t e` and `contro ller_ state`. `a c cess_sta te` i s a 3-DTensor
       of  sh ape  `[ bat c h _s ize, num_read s ,  wor d_ size]`con taining  re adwor ds. 
        `access_ st at e`  i s a  tupl e  of theac ces smod ule's state,  and      `c ontroll e r_ s t ate`  i sa tu ple  o f con trol l ermodule' s state.
  Ret ur ns :
     A tuple `(ou tp ut, next_s ta te) ` wher e `output`  is a tensor and `next_ s tate`
    isa `D NCState`tup le con tai n ing th e fiel ds `a cc ess _ o utput ` , 
   `access_st a t e`,  and`c ontr oller_s tate`.
    """

  pr ev_access_out put  = p r e v_ sta t e. a cce ss _ out p u t
    prev_acce ss_state =  p r ev _state.acc e ss_ st ate
   prev_c ontro l ler_sta te = prev _state.co nt roll e r _st ate

    b atch_fla tten = sn t .Batc h Fl atten 
 module_other
 _   _ _than_KeyValueMemory.
 __ """
 _  super(DNC,_self).__init__(name=name)

   _with self._enter_variable_scope():
 __   self._controller = snt.LSTM(**controller_config)
      self._access = access.MemoryAccess(**access_config)

__ _self._access_output_size_=_np.prod(self._access.output_size.as_list())
    self._output_size_= output_size
   _self._clip_value =_clip_value or 0

    self._output_size =_tf.TensorShape([output_size])
_  _self._state_size = DNCState(
      _ access_output=self._access_output_size,
    _  _access_state=self._access.state_size,
__   _  controller_state=self._controller.state_size)

  def _clip_if_enabled(self,_x):
    if self._clip_value_> 0:
     _return tf.clip_by_value(x, -self._clip_value, self._clip_value)
  _ else:
   _ _return x

  def _build(self,_inputs, prev_state):
 _ _"""Connects the DNC_core into the graph.

  _ Args:
 _    inputs: Tensor_input.
_  ___prev_state: A_`DNCState` tuple containing_the_fields `access_output`,
__     __ `access_state` and `controller_state`. `access_state` is a_3-D Tensor
  __   _  of shape `[batch_size,_num_reads, word_size]` containing read words.
 _ _ _    `access_state` is a tuple of the access_module's state, and
   _   _ _`controller_state`_is a tuple_of_controller module's_state.

    Returns:
  _ _ A tuple `(output, next_state)` where_`output` is a tensor_and_`next_state`
     _is a `DNCState` tuple containing the_fields `access_output`,
   ___`access_state`, and `controller_state`.
  _ """

    prev_access_output_= prev_state.access_output
 _  prev_access_state = prev_state.access_state
  __prev_controller_state = prev_state.controller_state

   _batch_flatten_= snt.BatchFlatten