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class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

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class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, data):

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class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, data):

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class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, data):

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class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, data):

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#!/usr/bin/env python


import collections
import requests
import config
import get_collectionids
from operator import itemgetter

def send_request(parameters, api):

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document_embeddings = DocumentRNNEmbeddings(word_embeddings, hidden_size=512, reproject_words=True, reproject_words_dimension=256, rnn_type='LSTM', rnn_layers=1, bidirectional=False)
classifier = TextClassifier(document_embeddings, label_dictionary=corpus.make_label_dictionary(), multi_label=False)
trainer = ModelTrainer(classifier, corpus)
trainer.train('./model', max_epochs=20, patience=5, mini_batch_size=32, learning_rate=0.1)

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def initialize_embeddings():
    """
    Summary:
        Stacks the list of pre-trained embedding vectors to be used as word representation (in concat.)
    Return:
        list: Returns list of pretrained embeddings vectors
    """
    word_embeddings = [
			WordEmbeddings('glove'),
			FlairEmbeddings('news-forward'),

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def segment_data(data_file):
    try:
        import pandas as pd
    except ImportError:
        raise
    
    data = pd.read_csv(data_file, encoding='latin-1').sample(frac=1).drop_duplicates()
    data = data[['classes', 'title']].rename(columns={"classes":"label", "title":"text"})
    data['label'] = '__label__' +data['label'].astype(str)
    data['text'] = data['text'].apply(lambda k: k.lower().strip())

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 import random  
 import os  
 from locust import HttpLocust, TaskSet, task

 TEST_DATA_PATH = 'test.csv' 
 
 def load_test_sentences():  
   utterances = []  
   with open(TEST_DATA_PATH, 'r') as fp:  
     for row in fp: