class_docstr: Partially load train_variables from a checkpoint.
  
  Hook used to load each variable saved in checkpoint into the graph. It
  will ignore any additional variables present in the graph that are not
  saved in the checkpoint. (Note: The loaded variables include ADAM/training
  variables, if they exist in the checkpoint)
  Can perform mapping if the base scopename for graph variables is different
  from the checkpoint variables.
code: def begin(self):
      # TODO(karishmamalkan): Add logging for when variables are loaded
      variable_references = {
          var.name: var for var in tf.all_variables()
      }
      variable_mappings = {}
      vars_in_chk = tf.train.list_variables(self.checkpoint_path)
      for var, _ in vars_in_chk:
          variable_mappings[var] = variable_references[
              var.replace(self.chk_scopename, self.graph_scopename)
              + ":0"
          ]
      tf.train.init_from_checkpoint(
          self.checkpoint_path, variable_mappings
      )
  
file_path: tensor2tensor/tensor2tensor/utils/partial_checkpoint_load_hook.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the PartialCheckpointLoad obj's underlying member method code with example usage: obj.begin() without return types