code: def sphere(levels_count=4):
      """This function generates cartesian coordinates (xyz) for a set
      of points forming a 3D sphere. The coordinates are expressed in
      meters and can be used as doas. The result has the format:
      (n_points, 3).
  
      Arguments
      ---------
      levels_count : int
          A number proportional to the number of points that the user
          wants to generate.
              - If levels_count = 1, then the sphere will have 42 points
              - If levels_count = 2, then the sphere will have 162 points
              - If levels_count = 3, then the sphere will have 642 points
              - If levels_count = 4, then the sphere will have 2562 points
              - If levels_count = 5, then the sphere will have 10242 points
              - ...
          By default, levels_count is set to 4.
  
      Example
      -------
      >>> import torch
      >>> from speechbrain.processing.multi_mic import sphere
      >>> doas = sphere()
      """
  
      # Generate points at level 0
  
      h = (5.0**0.5) / 5.0
      r = (2.0 / 5.0) * (5.0**0.5)
      pi = 3.141592654
  
      pts = torch.zeros((12, 3), dtype=torch.float)
      pts[0, :] = torch.FloatTensor([0, 0, 1])
      pts[11, :] = torch.FloatTensor([0, 0, -1])
      pts[range(1, 6), 0] = r * torch.sin(
          2.0 * pi * torch.arange(0, 5) / 5.0
      )
      pts[range(1, 6), 1] = r * torch.cos(
          2.0 * pi * torch.arange(0, 5) / 5.0
      )
      pts[range(1, 6), 2] = h
      pts[range(6, 11), 0] = (
          -1.0 * r * torch.sin(2.0 * pi * torch.arange(0, 5) / 5.0)
      )
      pts[range(6, 11), 1] = (
          -1.0 * r * torch.cos(2.0 * pi * torch.arange(0, 5) / 5.0)
      )
      pts[range(6, 11), 2] = -1.0 * h
  
      # Generate triangles at level 0
  
      trs = torch.zeros((20, 3), dtype=torch.long)
  
      trs[0, :] = torch.LongTensor([0, 2, 1])
      trs[1, :] = torch.LongTensor([0, 3, 2])
      trs[2, :] = torch.LongTensor([0, 4, 3])
      trs[3, :] = torch.LongTensor([0, 5, 4])
      trs[4, :] = torch.LongTensor([0, 1, 5])
  
      trs[5, :] = torch.LongTensor([9, 1, 2])
      trs[6, :] = torch.LongTensor([10, 2, 3])
      trs[7, :] = torch.LongTensor([6, 3, 4])
      trs[8, :] = torch.LongTensor([7, 4, 5])
      trs[9, :] = torch.LongTensor([8, 5, 1])
  
      trs[10, :] = torch.LongTensor([4, 7, 6])
      trs[11, :] = torch.LongTensor([5, 8, 7])
      trs[12, :] = torch.LongTensor([1, 9, 8])
      trs[13, :] = torch.LongTensor([2, 10, 9])
      trs[14, :] = torch.LongTensor([3, 6, 10])
  
      trs[15, :] = torch.LongTensor([11, 6, 7])
      trs[16, :] = torch.LongTensor([11, 7, 8])
      trs[17, :] = torch.LongTensor([11, 8, 9])
      trs[18, :] = torch.LongTensor([11, 9, 10])
      trs[19, :] = torch.LongTensor([11, 10, 6])
  
      # Generate next levels
  
      for levels_index in range(0, levels_count):
          #      0
          #     / \
          #    A---B
          #   / \ / \
          #  1---C---2
  
          trs_count = trs.shape[0]
          subtrs_count = trs_count * 4
  
          subtrs = torch.zeros((subtrs_count, 6), dtype=torch.long)
  
          subtrs[0 * trs_count + torch.arange(0, trs_count), 0] = trs[
              :, 0
          ]
          subtrs[0 * trs_count + torch.arange(0, trs_count), 1] = trs[
              :, 0
          ]
          subtrs[0 * trs_count + torch.arange(0, trs_count), 2] = trs[
              :, 0
          ]
          subtrs[0 * trs_count + torch.arange(0, trs_count), 3] = trs[
              :, 1
          ]
          subtrs[0 * trs_count + torch.arange(0, trs_count), 4] = trs[
              :, 2
          ]
          subtrs[0 * trs_count + torch.arange(0, trs_count), 5] = trs[
              :, 0
          ]
  
          subtrs[1 * trs_count + torch.arange(0, trs_count), 0] = trs[
              :, 0
          ]
          subtrs[1 * trs_count + torch.arange(0, trs_count), 1] = trs[
              :, 1
          ]
          subtrs[1 * trs_count + torch.arange(0, trs_count), 2] = trs[
              :, 1
          ]
          subtrs[1 * trs_count + torch.arange(0, trs_count), 3] = trs[
              :, 1
          ]
          subtrs[1 * trs_count + torch.arange(0, trs_count), 4] = trs[
              :, 1
          ]
          subtrs[1 * trs_count + torch.arange(0, trs_count), 5] = trs[
              :, 2
          ]
  
          subtrs[2 * trs_count + torch.arange(0, trs_count), 0] = trs[
              :, 2
          ]
          subtrs[2 * trs_count + torch.arange(0, trs_count), 1] = trs[
              :, 0
          ]
          subtrs[2 * trs_count + torch.arange(0, trs_count), 2] = trs[
              :, 1
          ]
          subtrs[2 * trs_count + torch.arange(0, trs_count), 3] = trs[
              :, 2
          ]
          subtrs[2 * trs_count + torch.arange(0, trs_count), 4] = trs[
              :, 2
          ]
          subtrs[2 * trs_count + torch.arange(0, trs_count), 5] = trs[
              :, 2
          ]
  
          subtrs[3 * trs_count + torch.arange(0, trs_count), 0] = trs[
              :, 0
          ]
          subtrs[3 * trs_count + torch.arange(0, trs_count), 1] = trs[
              :, 1
          ]
          subtrs[3 * trs_count + torch.arange(0, trs_count), 2] = trs[
              :, 1
          ]
          subtrs[3 * trs_count + torch.arange(0, trs_count), 3] = trs[
              :, 2
          ]
          subtrs[3 * trs_count + torch.arange(0, trs_count), 4] = trs[
              :, 2
          ]
          subtrs[3 * trs_count + torch.arange(0, trs_count), 5] = trs[
              :, 0
          ]
  
          subtrs_flatten = torch.cat(
              (subtrs[:, [0, 1]], subtrs[:, [2, 3]], subtrs[:, [4, 5]]),
              axis=0,
          )
          subtrs_sorted, _ = torch.sort(subtrs_flatten, axis=1)
  
          index_max = torch.max(subtrs_sorted)
  
          subtrs_scalar = (
              subtrs_sorted[:, 0] * (index_max + 1)
              + subtrs_sorted[:, 1]
          )
  
          unique_scalar, unique_indices = torch.unique(
              subtrs_scalar, return_inverse=True
          )
  
          unique_values = torch.zeros(
              (unique_scalar.shape[0], 2), dtype=unique_scalar.dtype
          )
  
          unique_values[:, 0] = torch.div(
              unique_scalar, index_max + 1, rounding_mode="floor"
          )
          unique_values[:, 1] = unique_scalar - unique_values[:, 0] * (
              index_max + 1
          )
  
          trs = torch.transpose(
              torch.reshape(unique_indices, (3, -1)), 0, 1
          )
  
          pts = (
              pts[unique_values[:, 0], :] + pts[unique_values[:, 1], :]
          )
          pts /= torch.repeat_interleave(
              torch.unsqueeze(torch.sum(pts**2, axis=1) ** 0.5, 1),
              3,
              1,
          )
  
      return pts
  
imports: import torch
  from packaging import version
  import speechbrain.processing.decomposition
label_desc: Use image node_id 4 for calling a global function with example usage: sphere(levels_count) and returns: pts