code: def jensen_shannon_divergence(x, y):
      result = 0.0
      l1_norm_x = 0.0
      l1_norm_y = 0.0
      dim = x.shape[0]
  
      for i in range(dim):
          l1_norm_x += x[i]
          l1_norm_y += y[i]
  
      l1_norm_x += FLOAT32_EPS * dim
      l1_norm_y += FLOAT32_EPS * dim
  
      pdf_x = (x + FLOAT32_EPS) / l1_norm_x
      pdf_y = (y + FLOAT32_EPS) / l1_norm_y
      m = 0.5 * (pdf_x + pdf_y)
  
      for i in range(dim):
          result += 0.5 * (
              pdf_x[i] * np.log(pdf_x[i] / m[i])
              + pdf_y[i] * np.log(pdf_y[i] / m[i])
          )
  
      return result
  
imports: import numpy
  import numba
  from pynndescent.optimal_transport import allocate_graph_structures, initialize_graph_structures, initialize_supply, initialize_cost, network_simplex_core, total_cost, ProblemStatus, sinkhorn_transport_plan
label_desc: Use image node_id 40 for calling a global function with example usage: jensen_shannon_divergence(x, y) and returns: result