code: def _eval_human_samples(self, batch, device):
      human_batch = {
          "origin": batch["origin"].clone().to(device),
          "direction": batch["direction"].clone().to(device),
          "near": batch["human_near"].clone().to(device),
          "far": batch["human_far"].clone().to(device),
      }
      human_samples = ray_utils.ray_to_samples(
          human_batch,
          self.opt.samples_per_ray,
          device=device,
          perturb=self.opt.perturb,
      )
      human_pts = human_samples[0]
      human_dirs = human_samples[1]
      human_z_vals = human_samples[2]
      human_b, human_n, _ = human_pts.shape
  
      # predict offset
      cur_time = torch.ones_like(human_pts[..., 0:1]) * batch[
          "cur_view_f"
      ].to(device)
      offset = random.choice(self.net.offset_nets)(
          torch.cat([human_pts, cur_time], dim=-1)
      )
  
      # warp points from observation space to canonical space
      mesh, raw_Ts = self.net.vertex_forward(int(batch["cap_id"]))
      human_pts = human_pts.reshape(-1, 3)
      Ts, _, _ = ray_utils.warp_samples_to_canonical_diff(
          human_pts.detach().cpu().numpy(),
          verts=mesh[0],
          faces=self.val_dataset.scene.captures[batch["cap_id"]]
          .posed_mesh_cpu.faces_packed()
          .numpy(),
          T=raw_Ts[0],
      )
      can_pts = (Ts @ ray_utils.to_homogeneous(human_pts)[..., None])[
          :, :3, 0
      ].reshape(human_b, human_n, 3)
      can_pts += offset
      can_dirs = can_pts[:, 1:] - can_pts[:, :-1]
      can_dirs = torch.cat([can_dirs, can_dirs[:, -1:]], dim=1)
      can_dirs = can_dirs / torch.norm(can_dirs, dim=2, keepdim=True)
      human_out = self.net.coarse_human_net(can_pts, can_dirs)
      return (
          human_pts,
          human_dirs,
          human_z_vals,
          can_pts,
          can_dirs,
          human_out,
      )
  
imports: import os
  import math
  import time
  import random
  import tqdm
  import torch
  import torch.nn.functional
  import numpy
  import torchvision.utils
  import igl
  import lpips
  from utils import utils, render_utils, ray_utils
  from trainers import tensorboard_helper
  from options import options
  from cameras.captures import ResizedPinholeCapture
  from cameras.pinhole_camera import PinholeCamera
  from models.vanilla import weight_reset
  from utils.constant import HARD_SURFACE_OFFSET, PATCH_SIZE, PATCH_SIZE_SQUARED, CANONICAL_ZOOM_FACTOR, CANONICAL_CAMERA_DIST
label_desc: Use image node_id 5 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj._eval_human_samples(batch, device) and returns: human_pts,  human_dirs,  human_z_vals,  can_pts,  can_dirs,  human_out