code: def _eval(self, d1, d2, d3, d4, binary_op, unary_op):
      if not binary_op:
  
          def binary_op(x, y):
              return x + y
  
      if not unary_op:
  
          def unary_op(x):
              return x
  
      if self.split_input:
          d1 = unary_op(d1)
          d2 = unary_op(d2)
          d3 = unary_op(d3)
          d4 = unary_op(d4)
      else:
          d1, d2, d3, d4 = (
              unary_op(d1),
              unary_op(d2),
              unary_op(d1 + 0.001),
              unary_op(d4),
          )
      a = binary_op(d1, d2)
      b = binary_op(d3, d4)
      c = a + b
      return c
  
imports: import itertools
  import operator
  import numpy
  import torch
  from .None import benchmark
label_desc: Use image node_id 2 for calling the BroadcastBench obj's underlying member method code with example usage: obj._eval(d1, d2, d3, d4, binary_op, unary_op) and returns: c, unknown, x