input: 
  from typing import Tuple
  
  def calculate_rRNA_percent(HTSEQ_COUNTS: str, GTF: str) -> Tuple[float, float]:
      """
      Calculate the percentage of transcripts mapping to rRNA and MT genes given an HTseq counts file and a GTF file.
      
      >>> calculate_rRNA_percent("C17_3_S20_Aligned.out.srt.bam_htseq_combined_counts.txt", "/Users/malwina/Documents/CTR-Data/genomes/Mus_musculus/mm10/Mus_musculus.GRCm38.84.gtf")
      (13.456, 7.892)
      >>> calculate_rRNA_percent("example_htseq_counts.txt", "/path/to/example_gtf.gtf")
      (5.321, 1.123)
      """
  
output:     import pandas as pd
      import re
  
      # Load the GTF file into a DataFrame
      gtf_df = pd.read_csv(GTF, sep="\t", comment="#", header=None, names=["seqname", "source", "feature", "start", "end", "score", "strand", "frame", "attribute"])
  
      # Extract gene_id and gene_biotype from the attribute column
      gtf_df['gene_id'] = gtf_df['attribute'].str.extract(r'gene_id "([^"]+)"')
      gtf_df['gene_biotype'] = gtf_df['attribute'].str.extract(r'gene_biotype "([^"]+)"')
  
      # Filter for rRNA and MT genes
      rrna_genes = set(gtf_df[gtf_df['gene_biotype'] == 'rRNA']['gene_id'])
      mt_genes = set(gtf_df[gtf_df['gene_biotype'] == 'Mt_rRNA']['gene_id'])
  
      # Load the HTSEQ_COUNTS file into a DataFrame
      htseq_df = pd.read_csv(HTSEQ_COUNTS, sep="\t", header=None, names=["gene_id", "count"])
  
      # Calculate the total counts
      total_counts = htseq_df['count'].sum()
  
      # Calculate the counts for rRNA and MT genes
      rrna_counts = htseq_df[htseq_df['gene_id'].isin(rrna_genes)]['count'].sum()
      mt_counts = htseq_df[htseq_df['gene_id'].isin(mt_genes)]['count'].sum()
  
      # Calculate the percentages
      rrna_percent = (rrna_counts / total_counts) * 100
      mt_percent = (mt_counts / total_counts) * 100
  
      return rrna_percent, mt_percent