code: def opt_func_info(func_name=None, signature=None):
      """
      Returns a dictionary containing the currently supported CPU dispatched
      features for all optimized functions.
  
      Parameters
      ----------
      func_name : str (optional)
          Regular expression to filter by function name.
  
      signature : str (optional)
          Regular expression to filter by data type.
  
      Returns
      -------
      dict
          A dictionary where keys are optimized function names and values are
          nested dictionaries indicating supported targets based on data types.
  
      Examples
      --------
      Retrieve dispatch information for functions named 'add' or 'sub' and
      data types 'float64' or 'float32':
  
      >>> dict = np.lib.introspect.opt_func_info(
      ...     func_name="add|abs", signature="float64|complex64"
      ... )
      >>> import json
      >>> print(json.dumps(dict, indent=2))
          {
            "absolute": {
              "dd": {
                "current": "SSE41",
                "available": "SSE41 baseline(SSE SSE2 SSE3)"
              },
              "Ff": {
                "current": "FMA3__AVX2",
                "available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)"
              },
              "Dd": {
                "current": "FMA3__AVX2",
                "available": "AVX512F FMA3__AVX2 baseline(SSE SSE2 SSE3)"
              }
            },
            "add": {
              "ddd": {
                "current": "FMA3__AVX2",
                "available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)"
              },
              "FFF": {
                "current": "FMA3__AVX2",
                "available": "FMA3__AVX2 baseline(SSE SSE2 SSE3)"
              }
            }
          }
  
      """
      from numpy._core._multiarray_umath import (
          __cpu_targets_info__ as targets,
          dtype,
      )
  
      if func_name is not None:
          func_pattern = re.compile(func_name)
          matching_funcs = {
              k: v for k, v in targets.items() if func_pattern.search(k)
          }
      else:
          matching_funcs = targets
  
      if signature is not None:
          sig_pattern = re.compile(signature)
          matching_sigs = {}
          for k, v in matching_funcs.items():
              matching_chars = {}
              for chars, targets in v.items():
                  if any(
                      [
                          sig_pattern.search(c)
                          or sig_pattern.search(dtype(c).name)
                          for c in chars
                      ]
                  ):
                      matching_chars[chars] = targets
              if matching_chars:
                  matching_sigs[k] = matching_chars
      else:
          matching_sigs = matching_funcs
      return matching_sigs
  
label_desc: Use image node_id 1 for calling a global function with example usage: opt_func_info(func_name, signature) and returns: matching_sigs