code: def test_plot_connectome_non_symmetric(
      node_coords, non_symmetric_matrix
  ):
      """Tests for plot_connectome with non symmetric adjacency matrices."""
      ax = plot_connectome(
          non_symmetric_matrix, node_coords, display_mode="ortho"
      )
      # No thresholding was performed, we should get
      # as many arrows as we have edges
      for direction in ["x", "y", "z"]:
          assert len(
              [
                  patch
                  for patch in ax.axes[direction].ax.patches
                  if isinstance(patch, FancyArrow)
              ]
          ) == np.prod(non_symmetric_matrix.shape)
  
      # Set a few elements of adjacency matrix to zero
      non_symmetric_matrix[1, 0] = 0.0
      non_symmetric_matrix[2, 3] = 0.0
      # Plot with different display mode
      ax = plot_connectome(
          non_symmetric_matrix, node_coords, display_mode="lzry"
      )
      # No edge in direction 'l' because of node coords
      assert (
          len(
              [
                  patch
                  for patch in ax.axes["l"].ax.patches
                  if isinstance(patch, FancyArrow)
              ]
          )
          == 0
      )
      for direction in ["z", "r", "y"]:
          assert (
              len(
                  [
                      patch
                      for patch in ax.axes[direction].ax.patches
                      if isinstance(patch, FancyArrow)
                  ]
              )
              == np.prod(non_symmetric_matrix.shape) - 2
          )
  
imports: import os
  import matplotlib.pyplot
  import numpy
  import pytest
  from matplotlib.patches import FancyArrow
  from scipy import sparse
  from nilearn.plotting import plot_connectome
label_desc: Use image node_id 15 for calling a global function with example usage: test_plot_connectome_non_symmetric(node_coords, non_symmetric_matrix) without return types