code: def generate_dummy_data_with_indicator(
      num_users=15000,
      num_items=30000,
      interaction_density=0.00045,
      pos_int_ratio=0.5,
  ):
      n_user_features = int(num_users * 1.2)
      n_user_tags = num_users * 3
      n_item_features = int(num_items * 1.2)
      n_item_tags = num_items * 3
      n_interactions = (num_users * num_items) * interaction_density
  
      user_features = sp.lil_matrix((num_users, n_user_features))
      for i in range(num_users):
          user_features[i, i] = 1
  
      for i in range(n_user_tags):
          user_features[
              random.randrange(num_users),
              random.randrange(num_users, n_user_features),
          ] = 1
  
      item_features = sp.lil_matrix((num_items, n_item_features))
      for i in range(num_items):
          item_features[i, i] = 1
  
      for i in range(n_item_tags):
          item_features[
              random.randrange(num_items),
              random.randrange(num_items, n_item_features),
          ] = 1
  
      interactions = sp.lil_matrix((num_users, num_items))
      for i in range(int(n_interactions * pos_int_ratio)):
          interactions[
              random.randrange(num_users), random.randrange(num_items)
          ] = 1
  
      for i in range(int(n_interactions * (1 - pos_int_ratio))):
          interactions[
              random.randrange(num_users), random.randrange(num_items)
          ] = -1
  
      return interactions, user_features, item_features
  
imports: import math
  import numpy
  import random
  import scipy.sparse
  import six
  import tensorflow
  from weakref import WeakKeyDictionary
  from .input_utils import create_tensorrec_dataset_from_sparse_matrix, create_tensorrec_dataset_from_tfrecord
label_desc: Use image node_id 5 for calling a global function with example usage: generate_dummy_data_with_indicator(num_users, num_items, interaction_density, pos_int_ratio) and returns: interactions,  user_features,  item_features