code: def load_tf_weights_in_gpt2(model, gpt2_checkpoint_path):
      
      try:
          import re
          import numpy as np
          import tensorflow as tf
      except ImportError:
          print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
              "https://www.tensorflow.org/install/ for installation instructions.")
          raise
      tf_path = os.path.abspath(gpt2_checkpoint_path)
      print("Converting TensorFlow checkpoint from {}".format(tf_path))
      # Load weights from TF model
      init_vars = tf.train.list_variables(tf_path)
      names = []
      arrays = []
      for name, shape in init_vars:
          print("Loading TF weight {} with shape {}".format(name, shape))
          array = tf.train.load_variable(tf_path, name)
          names.append(name)
          arrays.append(array.squeeze())
  
      for name, array in zip(names, arrays):
          name = name[6:]  # skip "model/"
          name = name.split('/')
          pointer = model
          for m_name in name:
              if re.fullmatch(r'[A-Za-z]+\d+', m_name):
                  l = re.split(r'(\d+)', m_name)
              else:
                  l = [m_name]
              if l[0] == 'w' or l[0] == 'g':
                  pointer = getattr(pointer, 'weight')
              elif l[0] == 'b':
                  pointer = getattr(pointer, 'bias')
              elif l[0] == 'wpe' or l[0] == 'wte':
                  pointer = getattr(pointer, l[0])
                  pointer = getattr(pointer, 'weight')
              else:
                  pointer = getattr(pointer, l[0])
              if len(l) >= 2:
                  num = int(l[1])
                  pointer = pointer[num]
          try:
              assert pointer.shape == array.shape
          except AssertionError as e:
              e.args += (pointer.shape, array.shape)
              raise
          print("Initialize PyTorch weight {}".format(name))
          pointer.data = torch.from_numpy(array)
      return model
docs: Load tf checkpoints in a pytorch model