code: def lbs(
      betas,
      pose,
      v_template,
      shapedirs,
      posedirs,
      J_regressor,
      parents,
      lbs_weights,
      pose2rot=True,
      dtype=torch.float32,
      return_T=False,
      concat_joints=False,
  ):
      """Performs Linear Blend Skinning with the given shape and pose parameters
  
      Parameters
      ----------
      betas : torch.tensor BxNB
          The tensor of shape parameters
      pose : torch.tensor Bx(J + 1) * 3
          The pose parameters in axis-angle format
      v_template torch.tensor BxVx3
          The template mesh that will be deformed
      shapedirs : torch.tensor 1xNB
          The tensor of PCA shape displacements
      posedirs : torch.tensor Px(V * 3)
          The pose PCA coefficients
      J_regressor : torch.tensor JxV
          The regressor array that is used to calculate the joints from
          the position of the vertices
      parents: torch.tensor J
          The array that describes the kinematic tree for the model
      lbs_weights: torch.tensor N x V x (J + 1)
          The linear blend skinning weights that represent how much the
          rotation matrix of each part affects each vertex
      pose2rot: bool, optional
          Flag on whether to convert the input pose tensor to rotation
          matrices. The default value is True. If False, then the pose tensor
          should already contain rotation matrices and have a size of
          Bx(J + 1)x9
      dtype: torch.dtype, optional
  
      Returns
      -------
      verts: torch.tensor BxVx3
          The vertices of the mesh after applying the shape and pose
          displacements.
      joints: torch.tensor BxJx3
          The joints of the model
      """
  
      batch_size = max(betas.shape[0], pose.shape[0])
      device = betas.device
  
      # Add shape contribution
      v_shaped = v_template + blend_shapes(betas, shapedirs)
  
      # Get the joints
      # NxJx3 array
      J = vertices2joints(J_regressor, v_shaped)
  
      # 3. Add pose blend shapes
      # N x J x 3 x 3
      ident = torch.eye(3, dtype=dtype, device=device)
      if pose2rot:
          rot_mats = batch_rodrigues(
              pose.view(-1, 3), dtype=dtype
          ).view([batch_size, -1, 3, 3])
  
          pose_feature = (rot_mats[:, 1:, :, :] - ident).view(
              [batch_size, -1]
          )
          # (N x P) x (P, V * 3) -> N x V x 3
          pose_offsets = torch.matmul(pose_feature, posedirs).view(
              batch_size, -1, 3
          )
      else:
          pose_feature = pose[:, 1:].view(batch_size, -1, 3, 3) - ident
          rot_mats = pose.view(batch_size, -1, 3, 3)
  
          pose_offsets = torch.matmul(
              pose_feature.view(batch_size, -1), posedirs
          ).view(batch_size, -1, 3)
  
      v_posed = v_shaped
  
      # 4. Get the global joint location
      J_transformed, A = batch_rigid_transform(
          rot_mats, J, parents, dtype=dtype
      )
  
      # 5. Do skinning:
      # W is N x V x (J + 1)
      W = lbs_weights.unsqueeze(dim=0).expand([batch_size, -1, -1])
      # (N x V x (J + 1)) x (N x (J + 1) x 16)
      num_joints = J_regressor.shape[0]
      T = torch.matmul(W, A.view(batch_size, num_joints, 16)).view(
          batch_size, -1, 4, 4
      )
  
      if return_T:
          if concat_joints:
              return torch.cat([T, A], dim=1), torch.cat(
                  [v_posed, J], dim=1
              )
          else:
              return T, v_posed
  
      homogen_coord = torch.ones(
          [batch_size, v_posed.shape[1], 1], dtype=dtype, device=device
      )
      v_posed_homo = torch.cat([v_posed, homogen_coord], dim=2)
      v_homo = torch.matmul(T, torch.unsqueeze(v_posed_homo, dim=-1))
  
      verts = v_homo[:, :, :3, 0]
  
      return verts, J_transformed
  
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 5 for calling a global function with example usage: lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents, lbs_weights, pose2rot, dtype, return_T, concat_joints) and returns: verts,  J_transformed, torch,  torch, T,  v_posed