docstr: For the neuralnetwork backend, the real_div is producing float output even for int inputs,
  while the mlprogram backend produces int type output.
code: def test_real_div_int_builder_to_backend(self, compute_unit, backend):
      """
      For the neuralnetwork backend, the real_div is producing float output even for int inputs,
      while the mlprogram backend produces int type output.
      """
      x = np.array([[10, 20, 30], [40, 50, 60]], dtype=np.float32)
      y = np.array([[11, 12, 13], [14, 15, 16]], dtype=np.float32)
  
      if backend.backend == "neuralnetwork":
          dtype = np.float32
      else:
          dtype = np.int32
      expected_outputs = np.array(x / y, dtype=dtype)
  
      build = lambda x, y: mb.real_div(x=x, y=y)
  
      expected_output_types = (2, 3, types.int32)
      input_placeholders = {
          "x": mb.placeholder(shape=x.shape, dtype=types.int32),
          "y": mb.placeholder(shape=y.shape, dtype=types.int32),
      }
      input_values = {"x": x, "y": y}
      run_compare_builder(
          build,
          input_placeholders,
          input_values,
          expected_output_types,
          expected_outputs,
          compute_unit=compute_unit,
          backend=backend,
      )
  
imports: import itertools
  import numpy
  import pytest
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import get_new_symbol, types
  from coremltools.converters.mil.mil.ops.tests.iOS14 import backends
  from coremltools.converters.mil.mil.ops.tests.testing_utils import run_compare_builder
  from coremltools.converters.mil.testing_reqs import compute_units
  from coremltools.converters.mil.testing_utils import ssa_fn
label_desc: Use image node_id 13 for calling the TestElementwiseBinary obj's underlying member method code with example usage: obj.test_real_div_int_builder_to_backend(compute_unit, backend) without return types