code: def compute_log_sum_exp(data, axes, keepdims):
      data_max = data.copy()
      ind = np.isinf(data_max)
      data_max[ind] = -np.inf
      mx = data_max.max(axis=axes, keepdims=True)
      sub = np.subtract(data, mx)
      exp = np.exp(sub, out=sub)
      mxs = np.sum(exp, axis=axes, keepdims=True, dtype=data.dtype)
      res = np.log(mxs) + mx
      if not keepdims:  # type: ignore
          res = np.squeeze(res, axis=axes)
      return (res,)
  
file_path: onnx/onnx/reference/ops/op_reduce_log_sum_exp.py
imports: import numpy
  from onnx.reference.ops._op import OpRunReduceNumpy
label_desc: Use image node_id 1 for calling a global function with example usage: compute_log_sum_exp(data, axes, keepdims) and returns: res