code: def get_sequence_strings(encoded_sequences):
      """
      Converts encoded sequences into an array with sequence strings
      """
      num_samples, _, _, seq_length = np.shape(encoded_sequences)
      sequence_characters = np.chararray((num_samples, seq_length))
      sequence_characters[:] = "N"
      for i, letter in enumerate(["A", "C", "G", "T"]):
          letter_indxs = (encoded_sequences[:, :, i, :] == 1).squeeze()
          sequence_characters[letter_indxs] = letter
      # return 1D view of sequence characters
      return sequence_characters.view("S%s" % (seq_length)).ravel()
  
file_path: deepchem/contrib/dragonn/utils.py
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import sys
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  from scipy.signal import correlate2d
  from simdna.simulations import loaded_motifs
label_desc: Use image node_id 5 for calling a global function with example usage: get_sequence_strings(encoded_sequences) and returns: sequence_characters