code: def _compute_similarities(instance, dataset):
      """
      Compute pairwise distances between an instance and all other data points
  
      Parameters
      ----------
      instance : 1D array
          Reference data point
      dataset : 2D array
          Entire dataset used to identify neighbors
  
      Returns
      -------
      similarity_distance : array
          V[j] == distance between actual instance and instance j
      """
      mean_vector = np.array(dataset, dtype=np.float32).std(axis=0)
      similarity_distance = np.zeros(dataset.shape[0])
  
      for j in range(0, dataset.shape[0]):
          # Calculate distance between point and instance j
          dist = _compute_distance(instance, dataset[j], mean_vector)
          similarity_distance[j] = dist
  
      return similarity_distance
  
file_path: shapash/shapash/utils/explanation_metrics.py
imports: import numpy
  import pandas
  from sklearn.preprocessing import normalize
label_desc: Use image node_id 3 for calling a global function with example usage: _compute_similarities(instance, dataset) and returns: similarity_distance