docstr: Check the results from packed_to_padded and PyTorch implementations
  are the same.
docstr_tok: ["Check","the","results","from","packed_to_padded","and","PyTorch","implementations","are","the","same","."]
code: def _test_packed_to_padded_helper(self, dims, device):
      """
      Check the results from packed_to_padded and PyTorch implementations
      are the same.
      """
      meshes = self.init_meshes(16, 100, 300, device=device)
      faces = meshes.faces_packed()
      mesh_to_faces_packed_first_idx = (
          meshes.mesh_to_faces_packed_first_idx()
      )
      max_faces = meshes.num_faces_per_mesh().max().item()
  
      if len(dims) == 0:
          values = torch.rand(
              (faces.shape[0],), device=device, requires_grad=True
          )
      else:
          values = torch.rand(
              (faces.shape[0], *dims), device=device, requires_grad=True
          )
      values_torch = values.detach().clone()
      values_torch.requires_grad = True
      values_padded = packed_to_padded(
          values, mesh_to_faces_packed_first_idx, max_faces
      )
      values_padded_torch = TestPackedToPadded.packed_to_padded_python(
          values_torch,
          mesh_to_faces_packed_first_idx,
          max_faces,
          device,
      )
      # check forward
      self.assertClose(values_padded, values_padded_torch)
  
      # check backward
      if len(dims) == 0:
          grad_inputs = torch.rand(
              (len(meshes), max_faces), device=device
          )
      else:
          grad_inputs = torch.rand(
              (len(meshes), max_faces, *dims), device=device
          )
      values_padded.backward(grad_inputs)
      grad_outputs = values.grad
      values_padded_torch.backward(grad_inputs)
      grad_outputs_torch1 = values_torch.grad
      grad_outputs_torch2 = TestPackedToPadded.padded_to_packed_python(
          grad_inputs,
          mesh_to_faces_packed_first_idx,
          values.size(0),
          device=device,
      )
      self.assertClose(grad_outputs, grad_outputs_torch1)
      self.assertClose(grad_outputs, grad_outputs_torch2)
  
imports: import unittest
  import torch
  from pytorch3d.ops import packed_to_padded, padded_to_packed
  from pytorch3d.structures.meshes import Meshes
  from .common_testing import get_random_cuda_device, TestCaseMixin
label_desc: Use image node_id 5 for calling the TestPackedToPadded obj's underlying member method code with example usage: obj._test_packed_to_padded_helper(dims, device) without return types