code: def visualize_3D_pose(test_data, label, result):
      fig = plt.figure(figsize=(19.2, 10.8))
      gs1 = gridspec.GridSpec(2, 6)  # 5 rows, 9 columns
      gs1.update(
          wspace=-0.00, hspace=0.05
      )  # set the spacing between axes.
      plt.axis("off")
  
      subplot_idx, exidx = 1, 1
      nsamples = 4
      for i in np.arange(nsamples):
          # Plot 2d pose
          ax1 = plt.subplot(gs1[subplot_idx - 1])
          p2d = test_data[exidx, :]
          show2Dpose(p2d, ax1)
          ax1.invert_yaxis()
  
          # Plot 3d gt
          ax2 = plt.subplot(gs1[subplot_idx], projection="3d")
          p3d = label[exidx, :]
          show3Dpose(p3d, ax2)
  
          # Plot 3d predictions
          ax3 = plt.subplot(gs1[subplot_idx + 1], projection="3d")
          p3d = result[exidx, :]
          show3Dpose(p3d, ax3, lcolor="#9b59b6", rcolor="#2ecc71")
  
          exidx = exidx + 1
          subplot_idx = subplot_idx + 3
  
      plt.show()
  
file_path: TensorLayer/tensorlayer/app/human_pose_estimation/common.py
imports: import tensorflow
  import numpy
  import pickle
  import matplotlib.pyplot
  import os
  import matplotlib.gridspec
label_desc: Use image node_id 6 for calling a global function with example usage: visualize_3D_pose(test_data, label, result) without return types