human
Can you provide a the Python code you requested given the following:
I am working on the file "06 - Natural Language Processing.ipynb".
The first task is:
Creating a Corpus
Building models requires gathering multiple documents and performing the processing steps on them that we showed above. We've used a tool called Baleen to ingest data from RSS feeds for the past year. (It currently contains 1,154,100 posts for 373 feeds after 5,566 jobs). 
We've provided a small sample of the corpus to start playing with the tool. It has saved documents in the following structure:

Each file stored in the directory of its category 
One document per file, stored as a pickle 
Document is a list of paragraphs 
Paragraph is a list of sentences 
Sentence is a list of (token, tag) tuples

We can then create a reader to automatically fetch data from our corpus. This is a bit more complex, but necessary. Also note that we add our normalization process here as well, just so we don't have to repeat steps later on.
Can you write Python code for it?
assistant
#Here is the Python code you requested.

import string
import pickle 

from nltk.corpus.reader.api import CorpusReader
from nltk.corpus.reader.api import CategorizedCorpusReader

CORPUS_PATH = "data/baleen_sample"
PKL_PATTERN = r'(?!\.)[a-z_\s]+/[a-f0-9]+\.pickle'
CAT_PATTERN = r'([a-z_\s]+)/.*'

class PickledCorpus(CategorizedCorpusReader, CorpusReader):
    
    def __init__(self, root, fileids=PKL_PATTERN, cat_pattern=CAT_PATTERN):
        CategorizedCorpusReader.__init__(self, {"cat_pattern": cat_pattern})
        CorpusReader.__init__(self, root, fileids)
        
        self.punct = set(string.punctuation) | {'“', '—', '’', '”', '…'}
        self.stopwords = set(nltk.corpus.stopwords.words('english'))
        self.wordnet = nltk.WordNetLemmatizer() 
    
    def _resolve(self, fileids, categories):
        if fileids is not None and categories is not None:
            raise ValueError("Specify fileids or categories, not both")

        if categories is not None:
            return self.fileids(categories=categories)
        
        if fileids is None:
            return self.fileids() 
        
        return fileids
    
    def lemmatize(self, token, tag):
        token = token.lower()
        
        if token not in self.stopwords:
            if not all(c in self.punct for c in token):
                tag =  {
                    'N': wn.NOUN,
                    'V': wn.VERB,
                    'R': wn.ADV,
                    'J': wn.ADJ
                }.get(tag[0], wn.NOUN)
                return self.wordnet.lemmatize(token, tag)
    
    def tokenize(self, doc):
        # Expects a preprocessed document, removes stopwords and punctuation
        # makes all tokens lowercase and lemmatizes them. 
        return list(filter(None, [
            self.lemmatize(token, tag)
            for paragraph in doc 
            for sentence in paragraph 
            for token, tag in sentence 
        ]))
    
    def docs(self, fileids=None, categories=None):
        # Resolve the fileids and the categories
        fileids =...