code: 
  
  """simple docstring"""
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						data
          UpperCAmelCase__ :       BinaryTreeNode | None						=						None
          UpperCAmelCase__ :       BinaryTreeNode | None						=						None
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   BinaryTreeNode | None:
        if node is None:
              UpperCAmelCase__ :       List[Any]						=						BinaryTreeNode(lowerCAmelCase						)
              return node
  
        # binary search tree is not empty,
        # so we will insert it into the tree
        # if new_value is less than value of data in node,
        #  add it to left subtree and proceed recursively
        if new_value < node.data:
              UpperCAmelCase__ :       Tuple						=						insert(node.left_child ,  lowerCAmelCase						)
        else:
              # if new_value is greater than value of data in node,
              #  add it to right subtree and proceed recursively
              UpperCAmelCase__ :       str						=						insert(node.right_child ,  lowerCAmelCase						)
        return node
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   list[int]:  # if node is None,return
        if node:
              UpperCAmelCase__ :       List[Any]						=						inorder(node.left_child						)
              UpperCAmelCase__ :       int						=						[*inorder_array, node.data]
              UpperCAmelCase__ :       Optional[Any]						=						inorder_array + inorder(node.right_child						)
        else:
              UpperCAmelCase__ :       List[Any]						=						[]
        return inorder_array
  
  
  
  
  def 				a__	(	)  ->   BinaryTreeNode | None:
        UpperCAmelCase__ :       Tuple						=						insert(lowerCAmelCase ,  15						)
        insert(lowerCAmelCase ,  10						)
        insert(lowerCAmelCase ,  25						)
        insert(lowerCAmelCase ,  6						)
        insert(lowerCAmelCase ,  14						)
        insert(lowerCAmelCase ,  20						)
        insert(lowerCAmelCase ,  60						)
        return root
  
  
  
  
  def 				a__	(	)  ->   None:
        # main function
        UpperCAmelCase__ :       Union[str, Any]						=						make_tree()
        print("""Printing values of binary search tree in Inorder Traversal."""						)
        inorder(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
      main()
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						GPTNeoConfig(
            hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
        print(F"""Building PyTorch model from configuration: {config}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained mesh-tf model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      _A						=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  