code: def build_eval_graph(input_fn, model_fn, hparams):
      """Build the evaluation computation graph."""
      dataset = input_fn(None)
      batch = dataset.make_one_shot_iterator().get_next()
  
      batch_holder = {
          "transform": tf.placeholder(
              tf.float32,
              [
                  1,
                  1,
                  hparams.n_parts,
                  hparams.n_dims + 1,
                  hparams.n_dims + 1,
              ],
          ),
          "joint": tf.placeholder(
              tf.float32, [1, 1, hparams.n_parts, hparams.n_dims]
          ),
          "point": tf.placeholder(
              tf.float32, [1, 1, None, hparams.n_dims]
          ),
          "label": tf.placeholder(tf.float32, [1, 1, None, 1]),
      }
      latent_holder, latent, occ = model_fn(
          batch_holder, None, None, "gen_mesh"
      )
  
      # Eval Summary
      iou_holder = tf.placeholder(tf.float32, [])
      best_holder = tf.placeholder(tf.float32, [])
      tf.summary.scalar("IoU", iou_holder)
      tf.summary.scalar("Best_IoU", best_holder)
  
      return {
          "batch_holder": batch_holder,
          "latent_holder": latent_holder,
          "latent": latent,
          "occ": occ,
          "batch": batch,
          "iou_holder": iou_holder,
          "best_holder": best_holder,
          "merged_summary": tf.summary.merge_all(),
      }
  
file_path: graphics/tensorflow_graphics/projects/nasa/eval.py
imports: from os import path
  import numpy
  import tensorflow.compat.v1
  from tensorflow_graphics.projects.nasa.lib import datasets
  from tensorflow_graphics.projects.nasa.lib import models
  from tensorflow_graphics.projects.nasa.lib import utils
label_desc: Use image node_id 1 for calling a global function with example usage: build_eval_graph(input_fn, model_fn, hparams) and returns: dict