code: def blend_shapes(betas, shape_disps):
      """Calculates the per vertex displacement due to the blend shapes
  
  
      Parameters
      ----------
      betas : torch.tensor Bx(num_betas)
          Blend shape coefficients
      shape_disps: torch.tensor Vx3x(num_betas)
          Blend shapes
  
      Returns
      -------
      torch.tensor BxVx3
          The per-vertex displacement due to shape deformation
      """
  
      # Displacement[b, m, k] = sum_{l} betas[b, l] * shape_disps[m, k, l]
      # i.e. Multiply each shape displacement by its corresponding beta and
      # then sum them.
      blend_shape = torch.einsum("bl,mkl->bmk", [betas, shape_disps])
      return blend_shape
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  import os.path
  import pickle
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 7 for calling a global function with example usage: blend_shapes(betas, shape_disps) and returns: blend_shape