(questions, nlp, timesteps)
return questions_tensor
Returns a time series of word vectors for tokens in the question

	Input:
	questions: list of unicode objects
	nlp: an instance of the class English() from spacy.en
	timesteps: the number of 

	Output:
	A numpy ndarray of shape: (nb_samples, timesteps, word_vec_dim)
Returns a time series of word vectors for tokens in the question
Returns a time series of word vectors for tokens in the question
def get_questions_tensor_timeseries(questions, nlp, timesteps):
	'''
	Returns a time series of word vectors for tokens in the question

	Input:
	questions: list of unicode objects
	nlp: an instance of the class English() from spacy.en
	timesteps: the number of 

	Output:
	A numpy ndarray of shape: (nb_samples, timesteps, word_vec_dim)
	'''
	assert not isinstance(questions, basestring)
	nb_samples = len(questions)
	word_vec_dim = nlp(questions[0])[0].vector.shape[0]
	questions_tensor = np.zeros((nb_samples, timesteps, word_vec_dim))
	for i in xrange(len(questions)):
		tokens = nlp(questions[i])
		for j in xrange(len(tokens)):
			if j<timesteps:
				questions_tensor[i,j,:] = tokens[j].vector

	return questions_tensor
def get_questions_tensor_timeseries ( questions , nlp , timesteps ) : assert not isinstance ( questions , basestring ) nb_samples = len ( questions ) word_vec_dim = nlp ( questions [ 0 ] ) [ 0 ] . vector . shape [ 0 ] questions_tensor = np . zeros ( ( nb_samples , timesteps , word_vec_dim ) ) for i in xrange ( len ( questions ) ) : tokens = nlp ( questions [ i ] ) for j in xrange ( len ( tokens ) ) : if j < timesteps : questions_tensor [ i , j , : ] = tokens [ j ] . vector return questions_tensor