code: def _unwrap_first_input_name(inbound_nodes):
      """Unwrap inbound_nodes three times to get first input name.
  
      Args:
        inbound_nodes: A str config that indicates input node. This method assumed
          the inbound_nodes looks like `[[['input', 0, 0, {}]]]`.
  
      Returns:
        Returns a str name for the first inbound node.
      """
      current = inbound_nodes
  
      for _ in range(3):
          if not current:
              return None
          if not isinstance(current, list):
              return None
          current = current[0]
  
      if isinstance(current, str):
          return current
  
      return None
  
imports: import warnings
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import metrics
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_annotate
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_aware_activation
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_config
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_layer
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_wrapper
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras import utils
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_scheme
  from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantize_registry
label_desc: Use image node_id 6 for calling a global function with example usage: _unwrap_first_input_name(inbound_nodes) and returns: None, current, None, None