Problem using spacy tokenizer for count vectorizer

I'm trying to do sentiment analysis on Amazon product reviews using the Spacy module for preprocessing the text data. The code I'm using is exactly this. I modified the dataset that I'm using according to what's shown in the link. I'm getting the error:

 TypeError       Traceback (most recent call last)
<ipython-input-139-bcbf2d3c9cce> in <module>
      4                  ('classifier', classifier)])
      5 # Fit our data
----> 6 pipe_countvect.fit(X_train,y_train)
      7 # Predicting with a test dataset
      8 sample_prediction = pipe_countvect.predict(X_test)

~\.conda\envs\py36\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
    328         """
    329         fit_params_steps = self._check_fit_params(**fit_params)
--> 330         Xt = self._fit(X, y, **fit_params_steps)
    331         with _print_elapsed_time('Pipeline',
    332                                  self._log_message(len(self.steps) - 1)):

~\.conda\envs\py36\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params_steps)
    294                 message_clsname='Pipeline',
    295                 message=self._log_message(step_idx),
--> 296                 **fit_params_steps[name])
    297             # Replace the transformer of the step with the fitted
    298             # transformer. This is necessary when loading the transformer

~\.conda\envs\py36\lib\site-packages\joblib\memory.py in __call__(self, *args, **kwargs)
    350 
    351     def __call__(self, *args, **kwargs):
--> 352         return self.func(*args, **kwargs)
    353 
    354     def call_and_shelve(self, *args, **kwargs):

~\.conda\envs\py36\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
    738     with _print_elapsed_time(message_clsname, message):
    739         if hasattr(transformer, 'fit_transform'):
--> 740             res = transformer.fit_transform(X, y, **fit_params)
    741         else:
    742             res = transformer.fit(X, y, **fit_params).transform(X)

~\.conda\envs\py36\lib\site-packages\sklearn\feature_extraction\text.py in fit_transform(self, raw_documents, y)
   1197 
   1198         vocabulary, X = self._count_vocab(raw_documents,
-> 1199                                           self.fixed_vocabulary_)
   1200 
   1201         if self.binary:

~\.conda\envs\py36\lib\site-packages\sklearn\feature_extraction\text.py in _count_vocab(self, raw_documents, fixed_vocab)
   1108         for doc in raw_documents:
   1109             feature_counter = {}
-> 1110             for feature in analyze(doc):
   1111                 try:
   1112                     feature_idx = vocabulary[feature]

~\.conda\envs\py36\lib\site-packages\sklearn\feature_extraction\text.py in _analyze(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)
    104             doc = preprocessor(doc)
    105         if tokenizer is not None:
--> 106             doc = tokenizer(doc)
    107         if ngrams is not None:
    108             if stop_words is not None:

TypeError: 'str' object is not callable

I'm not sure what's causing this error and how to get rid of it. I'm pretty sure the count vectorizer produces a sparse matrix and not a string one. One thing that I've considered is that I'm using the spacy tokenizer, which was used in the link as vectorizer = CountVectorizer(tokenizer = spacy_tokenizer, ngram_range=(1,1)) but when I ran the program it was saying that spacy_tokenizer was undefined. So I used vectorizer = CountVectorizer(tokenizer = 'spacy', ngram_range=(1,1)) instead. But if I remove this then I don't know how to use the spacy tokenizer, and either way I am not certain that this was indeed the cause of the problem. Please help me out!