code: def simulate_differential_accessibility(
      pos_motif_names,
      neg_motif_names,
      seq_length,
      min_num_motifs,
      max_num_motifs,
      num_pos,
      num_neg,
      GC_fraction,
  ):
      """
      Generates data for differential accessibility task.
  
      Parameters
      ----------
      pos_motif_names : list
          List of strings.
      neg_motif_names : list
          List of strings.
      seq_length : int
      min_num_motifs : int
      max_num_motifs : int
      num_pos : int
      num_neg : int
      GC_fraction : float
  
      Returns
      -------
      sequence_arr : 1darray
          Contains sequence strings.
      y : 1darray
          Contains labels.
      embedding_arr: 1darray
          Array of embedding objects.
      """
      (
          pos_motif_sequence_arr,
          _,
          positive_embedding_arr,
      ) = simulate_multi_motif_embedding(
          pos_motif_names,
          seq_length,
          min_num_motifs,
          max_num_motifs,
          num_pos,
          GC_fraction,
      )
      (
          neg_motif_sequence_arr,
          _,
          negative_embedding_arr,
      ) = simulate_multi_motif_embedding(
          neg_motif_names,
          seq_length,
          min_num_motifs,
          max_num_motifs,
          num_neg,
          GC_fraction,
      )
      sequence_arr = np.concatenate(
          (pos_motif_sequence_arr, neg_motif_sequence_arr)
      )
      y = np.array([[True]] * num_pos + [[False]] * num_neg)
      embedding_arr = positive_embedding_arr + negative_embedding_arr
      return sequence_arr, y, embedding_arr
  
file_path: deepchem/contrib/dragonn/simulations.py
imports: from __future__ import absolute_import, division, print_function
  from collections import OrderedDict
  import numpy
  import simdna
  from simdna.synthetic import RepeatedEmbedder, SubstringEmbedder, ReverseComplementWrapper, UniformPositionGenerator, InsideCentralBp, LoadedEncodeMotifs, PwmSamplerFromLoadedMotifs, UniformIntegerGenerator, ZeroOrderBackgroundGenerator, EmbedInABackground, GenerateSequenceNTimes, RandomSubsetOfEmbedders, IsInTraceLabelGenerator, EmbeddableEmbedder, PairEmbeddableGenerator
  from simdna.util import DiscreteDistribution
label_desc: Use image node_id 8 for calling a global function with example usage: simulate_differential_accessibility(pos_motif_names, neg_motif_names, seq_length, min_num_motifs, max_num_motifs, num_pos, num_neg, GC_fraction) and returns: sequence_arr,  y,  embedding_arr