text: # flake8: noqa
  from typing import Dict
  from scipy.special import expit
  
  initial_range = 0.1
  
  
  class Codec(object):
      """Translate between integer codes and characters."""
      def init(self, charset):
          charset = sorted(list(set(charset)))
          self.code2char = {}  # type: Dict[int, str]
          self.char2code = {}  # type:  Dict[str, int]
          for code,char in enumerate(charset):
              self.code2char[code] = char
              self.char2code[char] = code
          return self
      def size(self):
          """The total number of codes (use this for the number of output
          classes when training a classifier."""
          return len(list(self.code2char.keys()))
      def encode(self, s):
          "Encode the string `s` into a code sequence."
          dflt = self.char2code["~"]
          return [self.char2code.get(c,dflt) for c in s]
      def decode(self, l):
          "Decode a code sequence into a string."
          s = [self.code2char.get(c,"~") for c in l]
          return s
  
  class Network:
      def predict(self,xs):
          """Prediction is the same as forward propagation."""
          return self.forward(xs)
  
  class Softmax(Network):
      """A logistic regression network."""
      def __init__(self,Nh,No,initial_range=0.1,rand=None):
          pass
      def ninputs(self):
          pass
      def noutputs(self):
          pass
      def forward(self,ys):
          pass
      def backward(self,deltas):
          pass
  
  
  class LSTM(Network):
      """A standard LSTM network. This is a direct implementation of all the forward
      and backward propagation formulas, mainly for speed. (There is another, more
      abstract implementation as well, but that's significantly slower in Python
      due to function call overhead.)"""
      def __init__(self,ni,ns,initial=0.1,maxlen=5000):
          pass
  
      def init_weights(self,initial):
          pass
  
      def allocate(self,n):
          pass
  
      def reset(self,n):
          pass
  
      def forward(self,xs):
          pass
  
  ################################################################
  # combination classifiers
  ################################################################
  
  class Stacked(Network):
      """Stack two networks on top of each other."""
      def __init__(self,nets):
          self.nets = nets
      def forward(self,xs):
          pass
  
  class Reversed(Network):
      """Run a network on the time-reversed input."""
      def __init__(self,net):
          self.net = net
      def forward(self,xs):
          pass
  
  class Parallel(Network):
      """Run multiple networks in parallel on the same input."""
      def __init__(self,*nets):
          self.nets = nets
      def forward(self,xs):
          pass
  
  def BIDILSTM(Ni,Ns,No):
      """A bidirectional LSTM, constructed from regular and reversed LSTMs."""
      lstm1 = LSTM(Ni,Ns)
      lstm2 = Reversed(LSTM(Ni,Ns))
      bidi = Parallel(lstm1,lstm2)
      logreg = Softmax(2*Ns,No)
      stacked = Stacked([bidi,logreg])
      return stacked
  
  
  class SeqRecognizer(Network):
      """Perform sequence recognition using BIDILSTM and alignment."""
      def __init__(self,ninput,nstates,noutput=-1,codec=None,normalize=None):
          self.Ni = ninput
          if codec: noutput = codec.size()
          self.No = noutput
          self.lstm = BIDILSTM(ninput,nstates,noutput)
          self.codec = codec
      def translate_back(self, output):
          pass
      def translate_back_locations(self, output):
          pass
      def predictSequence(self,xs):
          "Predict an integer sequence of codes."
          pass
      def l2s(self,l):
          "Convert a code sequence into a unicode string after recognition."
          l = self.codec.decode(l)
          return u"".join(l)
      def predictString(self,xs):
          "Predict output as a string. This uses codec and normalizer."
          pass
  