code: def simulate_multi_motif_embedding(
      motif_names,
      seq_length,
      min_num_motifs,
      max_num_motifs,
      num_seqs,
      GC_fraction,
  ):
      """
      Generates data for multi motif recognition task.
  
      Parameters
      ----------
      motif_names : list
          List of strings.
      seq_length : int
      min_num_motifs : int
      max_num_motifs : int
      num_seqs : int
      GC_fraction : float
  
      Returns
      -------
      sequence_arr : 1darray
          Contains sequence strings.
      y : ndarray
          Contains labels for each motif.
      embedding_arr: 1darray
          Array of embedding objects.
      """
  
      def get_embedder(motif_name):
          substring_generator = PwmSamplerFromLoadedMotifs(
              loaded_motifs, motif_name
          )
          return SubstringEmbedder(
              ReverseComplementWrapper(substring_generator),
              name=motif_name,
          )
  
      embedders = [
          get_embedder(motif_name) for motif_name in motif_names
      ]
      quantity_generator = UniformIntegerGenerator(
          min_num_motifs, max_num_motifs
      )
      combined_embedder = [
          RandomSubsetOfEmbedders(quantity_generator, embedders)
      ]
      embed_in_background = EmbedInABackground(
          ZeroOrderBackgroundGenerator(
              seq_length,
              discreteDistribution=get_distribution(GC_fraction),
          ),
          combined_embedder,
      )
      generated_sequences = tuple(
          GenerateSequenceNTimes(
              embed_in_background, num_seqs
          ).generateSequences()
      )
      sequence_arr = np.array(
          [generated_seq.seq for generated_seq in generated_sequences]
      )
      label_generator = IsInTraceLabelGenerator(np.asarray(motif_names))
      y = np.array(
          [
              label_generator.generateLabels(generated_seq)
              for generated_seq in generated_sequences
          ],
          dtype=bool,
      )
      embedding_arr = [
          generated_seq.embeddings
          for generated_seq in generated_sequences
      ]
      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 7 for calling a global function with example usage: simulate_multi_motif_embedding(motif_names, seq_length, min_num_motifs, max_num_motifs, num_seqs, GC_fraction) and returns: sequence_arr,  y,  embedding_arr, SubstringEmbedder