code: def demonstrate_loading_weights_into_different_scope():
      print(
          "=" * 60
          + " Demonstrate loading weights saved in scopeQ, into variables now in scopeA"
      )
      tf.reset_default_graph()
      with tf.variable_scope("scopeA") as scope:
          m1a = Model1()
          print(
              "=" * 60
              + " Trying to load model1 weights from scopeQ into scopeA"
          )
          m1a.model.load(
              "model1_scopeQ.tfl",
              variable_name_map=("scopeA", "scopeQ"),
              verbose=True,
          )
  
imports: from __future__ import division, print_function, absolute_import
  import re
  import tflearn
  import tensorflow.compat.v1
  import tflearn.datasets.mnist
  from tflearn.layers.core import input_data, dropout, fully_connected
  from tflearn.layers.conv import conv_2d, max_pool_2d
  from tflearn.layers.normalization import local_response_normalization
  from tflearn.layers.estimator import regression
label_desc: Use image node_id 4 for calling a global function with example usage: demonstrate_loading_weights_into_different_scope() without return types