code: def init_uniform_y_rotations(batch_size: int, device: torch.device):
      """
      Generate a batch of `batch_size` 3x3 rotation matrices around y-axis
      whose angles are uniformly distributed between 0 and 2 pi.
      """
      axis = torch.tensor(
          [0.0, 1.0, 0.0], device=device, dtype=torch.float32
      )
      angles = torch.linspace(
          0, 2.0 * np.pi, batch_size + 1, device=device
      )
      angles = angles[:batch_size]
      log_rots = axis[None, :] * angles[:, None]
      R = so3_exp_map(log_rots)
      return R
  
imports: import unittest
  from functools import partial
  from itertools import product
  from typing import Tuple
  import numpy
  import torch
  from pytorch3d.ops import add_pointclouds_to_volumes, add_points_features_to_volume_densities_features
  from pytorch3d.ops.points_to_volumes import _points_to_volumes
  from pytorch3d.ops.sample_points_from_meshes import sample_points_from_meshes
  from pytorch3d.structures.meshes import Meshes
  from pytorch3d.structures.pointclouds import Pointclouds
  from pytorch3d.structures.volumes import Volumes
  from pytorch3d.transforms.so3 import so3_exp_map
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 3 for calling a global function with example usage: init_uniform_y_rotations(batch_size, device) and returns: R