code: def one_hot_encode(sequences):
      sequence_length = len(sequences[0])
      integer_type = (
          np.int8 if sys.version_info[0] == 2 else np.int32
      )  # depends on Python version
      integer_array = (
          LabelEncoder()
          .fit(np.array(("ACGTN",)).view(integer_type))
          .transform(sequences.view(integer_type))
          .reshape(len(sequences), sequence_length)
      )
      one_hot_encoding = OneHotEncoder(
          sparse=False, n_values=5, dtype=integer_type
      ).fit_transform(integer_array)
  
      return one_hot_encoding.reshape(
          len(sequences), 1, sequence_length, 5
      ).swapaxes(2, 3)[:, :, [0, 1, 2, 4], :]
  
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 3 for calling a global function with example usage: one_hot_encode(sequences) and returns: one_hot_encoding