class_docstr: Term Frequency - Inverse Document Frequency (TF-IDF) Recommender
  
  This class provides content-based recommendations using TF-IDF vectorization in combination with cosine similarity.
docstr: Initialize model parameters
  
  Args:
      id_col (str): Name of column containing item IDs.
      tokenization_method (str): ['none','nltk','bert','scibert'] option for tokenization method.
docstr_tok: ["Initialize","model","parameters","Args",":","id_col","(","str",")",":","Name","of","column","containing","item","IDs",".","tokenization_method","(","str",")",":","[","'none","'",",","'nltk","'",",","'bert","'",",","'scibert","'","]","option","for","tokenization","method","."]
code: def __init__(self, id_col, tokenization_method="scibert"):
      """Initialize model parameters
  
      Args:
          id_col (str): Name of column containing item IDs.
          tokenization_method (str): ['none','nltk','bert','scibert'] option for tokenization method.
      """
      self.id_col = id_col
      if tokenization_method.lower() not in [
          "none",
          "nltk",
          "bert",
          "scibert",
      ]:
          raise ValueError(
              'Tokenization method must be one of ["none" | "nltk" | "bert" | "scibert"]'
          )
      self.tokenization_method = tokenization_method.lower()
  
      # Initialize other variables used in this class
      self.tf = TfidfVectorizer()
      self.tfidf_matrix = dict()
      self.tokens = dict()
      self.stop_words = frozenset()
      self.recommendations = dict()
      self.top_k_recommendations = pd.DataFrame()
  
imports: from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.metrics.pairwise import linear_kernel
  from transformers import BertTokenizer
  import re
  import unicodedata
  import pandas
  import numpy
  import nltk
  from nltk.stem.porter import PorterStemmer
label_desc: Use image node_id 1 to create a new TfidfRecommender object  with example: obj = TfidfRecommender(id_col, tokenization_method)