docstr: Test with a batch where each batch element has one point
  where the normal and light direction are 45 degrees apart.
docstr_tok: ["Test","with","a","batch","where","each","batch","element","has","one","point","where","the","normal","and","light","direction","are","45","degrees","apart","."]
code: def test_diffuse_batched(self):
      """
      Test with a batch where each batch element has one point
      where the normal and light direction are 45 degrees apart.
      """
      batch_size = 10
      color = torch.tensor([1, 1, 1], dtype=torch.float32)
      direction = torch.tensor(
          [0, 1 / np.sqrt(2), 1 / np.sqrt(2)], dtype=torch.float32
      )
      normals = torch.tensor([0, 0, 1], dtype=torch.float32)
      expected_out = torch.tensor(
          [1 / np.sqrt(2), 1 / np.sqrt(2), 1 / np.sqrt(2)],
          dtype=torch.float32,
      )
  
      # Reshape
      direction = direction.view(-1, 3).expand(batch_size, -1)
      normals = normals.view(-1, 1, 3).expand(batch_size, -1, -1)
      color = color.view(-1, 3).expand(batch_size, -1)
      expected_out = expected_out.view(-1, 1, 3).expand(
          batch_size, 1, 3
      )
  
      lights = DirectionalLights(
          diffuse_color=color, direction=direction
      )
      output_light = lights.diffuse(normals=normals)
      self.assertClose(output_light, expected_out)
  
imports: import unittest
  import numpy
  import torch
  from pytorch3d.renderer.lighting import AmbientLights, DirectionalLights, PointLights
  from pytorch3d.transforms import RotateAxisAngle
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 3 for calling the TestDiffuseLighting obj's underlying member method code with example usage: obj.test_diffuse_batched() without return types