code: def _jiggle_within_stratas(bin_centers: torch.Tensor) -> torch.Tensor:
      """
      Performs sampling of 1 point per bin given the bin centers.
  
      More specifically, it replaces each point's value `z`
      with a sample from a uniform random distribution on
      `[z - delta_-, z + delta_+]`, where `delta_-` is half of the difference
      between `z` and the previous point, and `delta_+` is half of the difference
      between the next point and `z`. For the first and last items, the
      corresponding boundary deltas are assumed zero.
  
      Args:
          `bin_centers`: The input points of size (..., N); the result is broadcast
              along all but the last dimension (the rows). Each row should be
              sorted in ascending order.
  
      Returns:
          a tensor of size (..., N) with the locations jiggled within stratas/bins.
      """
      # Get intervals between bin centers.
      mids = 0.5 * (bin_centers[..., 1:] + bin_centers[..., :-1])
      upper = torch.cat((mids, bin_centers[..., -1:]), dim=-1)
      lower = torch.cat((bin_centers[..., :1], mids), dim=-1)
      # Samples in those intervals.
      jiggled = lower + (upper - lower) * torch.rand_like(lower)
      return jiggled
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import torch
  from pytorch3d.common.compat import meshgrid_ij
  from pytorch3d.ops import padded_to_packed
  from pytorch3d.renderer.cameras import CamerasBase
  from pytorch3d.renderer.implicit.utils import HeterogeneousRayBundle, RayBundle
  from torch.nn import functional
label_desc: Use image node_id 5 for calling a global function with example usage: _jiggle_within_stratas(bin_centers) and returns: jiggled