code: def compute_opaqueness_mask(weights, depth_threshold=0.5):
      """Computes a mask which will be 1.0 at the depth point.
  
      Args:
        weights: the density weights from NeRF.
        depth_threshold: the accumulation threshold which will be used as the depth
          termination point.
  
      Returns:
        A tensor containing a mask with the same size as weights that has one
          element long the sample dimension that is 1.0. This element is the point
          where the 'surface' is.
      """
      cumulative_contribution = jnp.cumsum(weights, axis=-1)
      depth_threshold = jnp.array(depth_threshold, dtype=weights.dtype)
      opaqueness = cumulative_contribution >= depth_threshold
      false_padding = jnp.zeros_like(opaqueness[..., :1])
      padded_opaqueness = jnp.concatenate(
          [false_padding, opaqueness[..., :-1]], axis=-1
      )
      opaqueness_mask = jnp.logical_xor(opaqueness, padded_opaqueness)
      opaqueness_mask = opaqueness_mask.astype(weights.dtype)
      return opaqueness_mask
  
file_path: nerfies/nerfies/model_utils.py
imports: from flax import linen
  from flax import optim
  from flax import struct
  from jax import lax
  from jax import random
  import jax.numpy
label_desc: Use image node_id 5 for calling a global function with example usage: compute_opaqueness_mask(weights, depth_threshold) and returns: opaqueness_mask