class_docstr: Basic idea pulled from variable.load. Possible this will change in 2.X so worth tracking what
  that fn changes too and mirror it here.
class_docstr_tok: ["Basic","idea","pulled","from","variable.load",".","Possible","this","will","change","in","2.X","so","worth","tracking","what","that","fn","changes","too","and","mirror","it","here","."]
code: def add(self, var, val):
      if (
          var.name.endswith("we:0")
          or "bert/embeddings/position_embedding" in var.name
      ):  # for backwards comaptibility with pre-saved models
          val = val[: var.shape[0]]
      if hasattr(var, "_values"):
          underlying_vars = var._values
      else:
          underlying_vars = [var]
      for v in underlying_vars:
          if v.initializer not in self.ops:
              self.ops.append(v.initializer)
          self.feed[v.initializer.inputs[1]] = val
  
imports: import os
  from concurrent.futures import ThreadPoolExecutor
  import logging
  import sys
  import re
  import joblib
  import numpy
  import tensorflow
  from tensorflow.compat.v1.train import SessionRunHook
  from tensorflow_estimator.python.estimator.early_stopping import _StopOnPredicateHook
  from finetune.errors import FinetuneError
  from finetune.config import get_config
  from finetune.util.metrics import read_eval_metrics
label_desc: Use image node_id 3 for calling the BatchedVarLoad obj's underlying member method code with example usage: obj.add(var, val) without return types