class_docstr: Memory component in a memory network.
  
  Args:
      A (chainer.links.EmbedID): Embed matrix for input. Its shape is
          ``(n_vocab, n_units)``.
      C (chainer.links.EmbedID): Embed matrix for output. Its shape is
          ``(n_vocab, n_units)``.
      TA (chainer.links.EmbedID): Embed matrix for temporal encoding for
          input. Its shape is ``(max_memory, n_units)``.
      TC (chainer.links.EmbedID): Embed matrix for temporal encoding for
          output. Its shape is ``(max_memory, n_units)``.
      encoder (callable): It encodes given stentences to embed vectors.
class_docstr_tok: ["Memory","component","in","a","memory","network",".","Args",":","A","(","chainer.links.EmbedID",")",":","Embed","matrix","for","input",".","Its","shape","is","``","(","n_vocab",",","n_units",")","``",".","C","(","chainer.links.EmbedID",")",":","Embed","matrix","for","output",".","Its","shape","is","``","(","n_vocab",",","n_units",")","``",".","TA","(","chainer.links.EmbedID",")",":","Embed","matrix","for","temporal","encoding","for","input",".","Its","shape","is","``","(","max_memory",",","n_units",")","``",".","TC","(","chainer.links.EmbedID",")",":","Embed","matrix","for","temporal","encoding","for","output",".","Its","shape","is","``","(","max_memory",",","n_units",")","``",".","encoder","(","callable",")",":","It","encodes","given","stentences","to","embed","vectors","."]
code: def __init__(self, A, C, TA, TC, encoder):
      self.A = A
      self.C = C
      self.TA = TA
      self.TC = TC
      self.encoder = encoder
  
imports: import collections
  import json
  import os
  import numpy
  import six
  import chainer
  from chainer import backend
  import chainer.functions
  from chainer import initializers
  import chainer.links
  import babi
label_desc: Use image node_id 1 to create a new Memory object from inherited base classes: object with example: obj = Memory(A, C, TA, TC, encoder)