code: def simulate_motif_counting(
      motif_name,
      seq_length,
      pos_counts,
      neg_counts,
      num_pos,
      num_neg,
      GC_fraction,
  ):
      """
      Generates data for motif counting task.
  
      Parameters
      ----------
      motif_name : str
      seq_length : int
      pos_counts : list
          (min_counts, max_counts) for positive set.
      neg_counts : list
          (min_counts, max_counts) for negative set.
      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_count_sequence_array, positive_embedding_arr = motif_density(
          motif_name,
          seq_length,
          num_pos,
          pos_counts[0],
          pos_counts[1],
          GC_fraction,
      )
      neg_count_sequence_array, negative_embedding_arr = motif_density(
          motif_name,
          seq_length,
          num_pos,
          neg_counts[0],
          neg_counts[1],
          GC_fraction,
      )
      sequence_arr = np.concatenate(
          (pos_count_sequence_array, neg_count_sequence_array)
      )
      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 5 for calling a global function with example usage: simulate_motif_counting(motif_name, seq_length, pos_counts, neg_counts, num_pos, num_neg, GC_fraction) and returns: sequence_arr,  y,  embedding_arr