class_docstr: seq2seq recurrent neural network, implemented using TFLearn.
class_docstr_tok: ["seq2seq","recurrent","neural","network",",","implemented","using","TFLearn","."]
docstr: Generate training dataset.  Produce random (integer) sequences X, and corresponding
  expected output sequences Y = generate_output_sequence(X).
  
  Return xy_data, y_data (both of type uint32)
  
  xy_data = numpy array of shape [num_points, in_seq_len + out_seq_len], with each point being X + Y
  y_data  = numpy array of shape [num_points, out_seq_len]
docstr_tok: ["Generate","training","dataset",".","Produce","random","(","integer",")","sequences","X",",","and","corresponding","expected","output","sequences","Y","=","generate_output_sequence","(","X",")",".","Return","xy_data",",","y_data","(","both","of","type","uint32",")","xy_data","=","numpy","array","of","shape","[","num_points",",","in_seq_len","+","out_seq_len","]",",","with","each","point","being","X","+","Y","y_data","=","numpy","array","of","shape","[","num_points",",","out_seq_len","]"]
code: def generate_trainig_data(self, num_points):
      """
      Generate training dataset.  Produce random (integer) sequences X, and corresponding
      expected output sequences Y = generate_output_sequence(X).
  
      Return xy_data, y_data (both of type uint32)
  
      xy_data = numpy array of shape [num_points, in_seq_len + out_seq_len], with each point being X + Y
      y_data  = numpy array of shape [num_points, out_seq_len]
      """
      x_data = np.random.randint(
          0, self.in_max_int, size=(num_points, self.in_seq_len)
      )  # shape [num_points, in_seq_len]
      x_data = x_data.astype(np.uint32)  # ensure integer type
  
      y_data = [
          self.sequence_pattern.generate_output_sequence(x)
          for x in x_data
      ]
      y_data = np.array(y_data)
  
      xy_data = np.append(
          x_data, y_data, axis=1
      )  # shape [num_points, 2*seq_len]
      return xy_data, y_data
  
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 2 for calling the TFLearnSeq2Seq obj's underlying member method code with example usage: obj.generate_trainig_data(num_points) and returns: xy_data,  y_data