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: Get full information for a single recommended item.
  
  Args:
      metadata (pandas.DataFrame): Dataframe containing item info.
      rec_id (str): Identifier for recommended item.
  
  Returns:
      pandas.Series: Single row from dataframe containing recommended item info.
docstr_tok: ["Get","full","information","for","a","single","recommended","item",".","Args",":","metadata","(","pandas.DataFrame",")",":","Dataframe","containing","item","info",".","rec_id","(","str",")",":","Identifier","for","recommended","item",".","Returns",":","pandas.Series",":","Single","row","from","dataframe","containing","recommended","item","info","."]
code: def __get_single_item_info(self, metadata, rec_id):
      """Get full information for a single recommended item.
  
      Args:
          metadata (pandas.DataFrame): Dataframe containing item info.
          rec_id (str): Identifier for recommended item.
  
      Returns:
          pandas.Series: Single row from dataframe containing recommended item info.
      """
  
      # Return row
      rec_info = metadata.iloc[
          int(np.where(metadata[self.id_col] == rec_id)[0])
      ]
  
      return rec_info
  
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 11 for calling the TfidfRecommender obj's underlying member method code with example usage: obj.__get_single_item_info(metadata, rec_id) and returns: rec_info