class_docstr: seq2seq recurrent neural network, implemented using TFLearn.
class_docstr_tok: ["seq2seq","recurrent","neural","network",",","implemented","using","TFLearn","."]
docstr: sequence_pattern_class = a SequencePattern class instance, which defines pattern parameters 
                           (input, output lengths, name, generating function)
  seq2seq_model = string specifying which seq2seq model to use, e.g. "embedding_rnn"
docstr_tok: ["sequence_pattern_class","=","a","SequencePattern","class","instance",",","which","defines","pattern","parameters","(","input",",","output","lengths",",","name",",","generating","function",")","seq2seq_model","=","string","specifying","which","seq2seq","model","to","use",",","e.g",".","``","embedding_rnn","''"]
code: def __init__(
      self,
      sequence_pattern,
      seq2seq_model=None,
      verbose=None,
      name=None,
      data_dir=None,
  ):
      """
      sequence_pattern_class = a SequencePattern class instance, which defines pattern parameters
                               (input, output lengths, name, generating function)
      seq2seq_model = string specifying which seq2seq model to use, e.g. "embedding_rnn"
      """
      self.sequence_pattern = sequence_pattern
      self.seq2seq_model = seq2seq_model or "embedding_rnn"
      assert self.seq2seq_model in self.AVAILABLE_MODELS
      self.in_seq_len = self.sequence_pattern.INPUT_SEQUENCE_LENGTH
      self.out_seq_len = self.sequence_pattern.OUTPUT_SEQUENCE_LENGTH
      self.in_max_int = self.sequence_pattern.INPUT_MAX_INT
      self.out_max_int = self.sequence_pattern.OUTPUT_MAX_INT
      self.verbose = verbose or 0
      self.n_input_symbols = self.in_max_int + 1
      self.n_output_symbols = (
          self.out_max_int + 2
      )  # extra one for GO symbol
      self.model_instance = None
      self.name = name
      self.data_dir = data_dir
  
imports: from __future__ import division, print_function
  import os
  import sys
  import tflearn
  import argparse
  import json
  import numpy
  import tensorflow.compat.v1
  from tensorflow.contrib.legacy_seq2seq.python.ops import seq2seq
  from tensorflow.python.ops import rnn_cell
label_desc: Use image node_id 1 to create a new TFLearnSeq2Seq object from inherited base classes: object with example: obj = TFLearnSeq2Seq(sequence_pattern, seq2seq_model, verbose, name, data_dir)