code: def _chamfer_distance_single_direction(
      x,
      y,
      x_lengths,
      y_lengths,
      x_normals,
      y_normals,
      weights,
      batch_reduction: Union[str, None],
      point_reduction: Union[str, None],
      norm: int,
      abs_cosine: bool,
  ):
      return_normals = x_normals is not None and y_normals is not None
  
      N, P1, D = x.shape
  
      # Check if inputs are heterogeneous and create a lengths mask.
      is_x_heterogeneous = (x_lengths != P1).any()
      x_mask = (
          torch.arange(P1, device=x.device)[None] >= x_lengths[:, None]
      )  # shape [N, P1]
      if y.shape[0] != N or y.shape[2] != D:
          raise ValueError("y does not have the correct shape.")
      if weights is not None:
          if weights.size(0) != N:
              raise ValueError("weights must be of shape (N,).")
          if not (weights >= 0).all():
              raise ValueError("weights cannot be negative.")
          if weights.sum() == 0.0:
              weights = weights.view(N, 1)
              if batch_reduction in ["mean", "sum"]:
                  return (
                      (x.sum((1, 2)) * weights).sum() * 0.0,
                      (x.sum((1, 2)) * weights).sum() * 0.0,
                  )
              return (
                  (x.sum((1, 2)) * weights) * 0.0,
                  (x.sum((1, 2)) * weights) * 0.0,
              )
  
      cham_norm_x = x.new_zeros(())
  
      x_nn = knn_points(
          x, y, lengths1=x_lengths, lengths2=y_lengths, norm=norm, K=1
      )
      cham_x = x_nn.dists[..., 0]  # (N, P1)
  
      if is_x_heterogeneous:
          cham_x[x_mask] = 0.0
  
      if weights is not None:
          cham_x *= weights.view(N, 1)
  
      if return_normals:
          # Gather the normals using the indices and keep only value for k=0
          x_normals_near = knn_gather(y_normals, x_nn.idx, y_lengths)[
              ..., 0, :
          ]
  
          cosine_sim = F.cosine_similarity(
              x_normals, x_normals_near, dim=2, eps=1e-6
          )
          # If abs_cosine, ignore orientation and take the absolute value of the cosine sim.
          cham_norm_x = 1 - (
              torch.abs(cosine_sim) if abs_cosine else cosine_sim
          )
  
          if is_x_heterogeneous:
              cham_norm_x[x_mask] = 0.0
  
          if weights is not None:
              cham_norm_x *= weights.view(N, 1)
  
      if point_reduction is not None:
          # Apply point reduction
          cham_x = cham_x.sum(1)  # (N,)
          if return_normals:
              cham_norm_x = cham_norm_x.sum(1)  # (N,)
          if point_reduction == "mean":
              x_lengths_clamped = x_lengths.clamp(min=1)
              cham_x /= x_lengths_clamped
              if return_normals:
                  cham_norm_x /= x_lengths_clamped
  
          if batch_reduction is not None:
              # batch_reduction == "sum"
              cham_x = cham_x.sum()
              if return_normals:
                  cham_norm_x = cham_norm_x.sum()
              if batch_reduction == "mean":
                  div = (
                      weights.sum()
                      if weights is not None
                      else max(N, 1)
                  )
                  cham_x /= div
                  if return_normals:
                      cham_norm_x /= div
  
      cham_dist = cham_x
      cham_normals = cham_norm_x if return_normals else None
      return cham_dist, cham_normals
  
file_path: pytorch3d/pytorch3d/loss/chamfer.py
imports: from typing import Union
  import torch
  import torch.nn.functional
  from pytorch3d.ops.knn import knn_gather, knn_points
  from pytorch3d.structures.pointclouds import Pointclouds
label_desc: Use image node_id 3 for calling a global function with example usage: _chamfer_distance_single_direction(x, y, x_lengths, y_lengths, x_normals, y_normals, weights, batch_reduction, point_reduction, norm, abs_cosine) and returns: cham_dist,  cham_normals, unknown,  unknown, unknown,  unknown