code: def get_feature_distribution_similar_status(
      data_meta, feature_name, dataset_a, dataset_b
  ):
      sample_label_key = list(data_meta[dataset_a].keys())[0]
      dist_a = data_meta[dataset_a][sample_label_key][
          constants.KEY_CATEGORICAL_FEATURE_DISTRIBUTION
      ][feature_name]["all"]
      dist_b = data_meta[dataset_b][sample_label_key][
          constants.KEY_CATEGORICAL_FEATURE_DISTRIBUTION
      ][feature_name]["all"]
      key_names, size_a, size_b, overlap = _get_union_keyset(
          dist_a, dist_b
      )
      normalized_dist_a = _get_normalized_distribution(
          dist_a, key_names
      )
      normalized_dist_b = _get_normalized_distribution(
          dist_b, key_names
      )
  
      cos_sim = cosine_similarity(
          [normalized_dist_a], [normalized_dist_b]
      )[0, 0]
  
      if (
          cos_sim
          < constants.RECOMMEND_FEATURE_DISTRIBUTION_DISTANCE_BENCHMARK
      ):
          status = False
      else:
          status = True
  
      return status, key_names, cos_sim, size_a, size_b, overlap
  
file_path: contextual-ai/xai/recommendations/recommendation.py
imports: import xai.constants
  from sklearn.metrics.pairwise import cosine_similarity
  import math
label_desc: Use image node_id 6 for calling a global function with example usage: get_feature_distribution_similar_status(data_meta, feature_name, dataset_a, dataset_b) and returns: status,  key_names,  cos_sim,  size_a,  size_b,  overlap