class_docstr: Layer to transform a depth image into a point cloud
      
class_docstr_tok: ["Layer","to","transform","a","depth","image","into","a","point","cloud"]
code: def predict(self, depth, inv_K, **kwargs):
      cam_points = mx.nd.batch_dot(
          inv_K[:, :3, :3], self.pix_coords.data()
      )
      cam_points = depth.reshape(self.batch_size, 1, -1) * cam_points
      cam_points = mx.nd.concat(cam_points, self.ones.data(), dim=1)
  
      return cam_points
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import mxnet
  from mxnet.gluon import nn
label_desc: Use image node_id 3 for calling the BackprojectDepth obj's underlying member method code with example usage: obj.predict(depth, inv_K) and returns: cam_points