class_docstr: Computes the natural logarithm of the sum of the exponentials of the elements across
  given dimensions of the input tensor. It is a smooth approximation of the maximum
  function, more numerically stable than ``log(sum(exp(input)))``. It avoids
  overflows caused by taking the ``exp`` of large inputs and underflows caused by
  taking the ``log`` of small inputs.
  
  Parameters
  ----------
  x: <\*,T> (Required)
      * Must be 1-dimensional or higher.
  
  axes: const<K,i32> (Optional, default="None", reduce on all axes.)
      * The dimensions to reduce.
  
  keep_dims: const<bool> (Optional, default=False)
      * If ``False``, the rank is reduced by ``1`` for each entry in ``axes``,
        otherwise retain reduced axes with length ``1``.
  
  Returns
  -------
  <\*,T>
      * Scalar or tensor: The reduced tensor.
  
  Attributes
  ----------
  T: i32, fp16, fp32
  
  References
  ----------
  See `tf.math.reduce_logsumexp <https://www.tensorflow.org/api_docs/python/tf/math/reduce_logsumexp>`_.
code: def get_operator(self):
      def operator(a, axis=None, keepdims=False):
          max_values = np.amax(a, axis=axis, keepdims=True)
          temp = np.exp(a - max_values)
  
          if not keepdims:
              max_values = np.squeeze(max_values, axis=axis)
  
          sum = np.sum(temp, axis=axis, keepdims=keepdims)
          result = np.log(sum)
          return result + max_values
  
      return operator
  
imports: import numpy
  from coremltools.converters.mil.mil import Operation, precondition, types
  from coremltools.converters.mil.mil.input_type import DefaultInputs, InputSpec, TensorInputType
  from coremltools.converters.mil.mil.operation import VALUE
  from coremltools.converters.mil.mil.ops.defs._op_reqs import register_op
  from coremltools.converters.mil.mil.types import nptype_from_builtin
label_desc: Use image node_id 1 for calling the reduce_log_sum_exp obj's underlying member method code with example usage: obj.get_operator() and returns: operator, unknown