code: def _read(
      filename,
      schema,
      seq_label='sequence',
      alphabet=None,
      use_uids=True,
      **kwargs):
      
      # Check Alphabet if given
      if alphabet is None:
          alphabet = Bio.Alphabet.Alphabet()
  
      elif alphabet in ['dna', 'rna', 'protein', 'nucleotide']:
          alphabet = getattr(Bio.Alphabet, 'generic_{}'.format(alphabet))
  
      else:
          raise Exception(
              "The alphabet is not recognized. Must be 'dna', 'rna', "
              "'nucleotide', or 'protein'.")
  
      kwargs.update(alphabet=alphabet)
  
      # Prepare DataFrame fields.
      data = {
          'id': [],
          seq_label: [],
          'description': [],
          'label': []
      }
      if use_uids:
          data['uid'] = []
  
      # Parse Fasta file.
      for i, s in enumerate(SeqIO.parse(filename, format=schema, **kwargs)):
          data['id'].append(s.id)
          data[seq_label].append(str(s.seq))
          data['description'].append(s.description)
          data['label'].append(s.name)
  
          if use_uids:
              data['uid'].append(get_random_id(10))
  
      # Port to DataFrame.
      return pd.DataFrame(data)
docs: Use BioPython's sequence parsing module to convert any file format to
      a Pandas DataFrame.
  
      The resulting DataFrame has the following columns:
          - name
          - id
          - description
          - sequence