code: def test_builder_to_backend_stress(
      self, compute_unit, rank, broadcast, backend
  ):
      equation = "abcd,adce->abce" if rank == 4 else "vnm,mno->vno"
      shape_x = np.random.randint(low=2, high=16, size=rank).astype(
          np.int32
      )
      shape_y = np.random.randint(low=2, high=12, size=rank).astype(
          np.int32
      )
      shape_y[-3] = shape_x[-1]
      shape_y[-2] = 1 if broadcast else shape_x[-2]
      if rank == 4:
          shape_x[-4] = 1 if broadcast else shape_y[-4]
  
      x_val = np.random.rand(*shape_x)
      y_val = np.random.rand(*shape_y)
      input_placeholder_dict = {
          "x": mb.placeholder(shape=x_val.shape),
          "y": mb.placeholder(shape=y_val.shape),
      }
  
      input_value_dict = {"x": x_val, "y": y_val}
      out_shape = (
          [shape_y[-4], shape_x[-3], shape_x[-2], shape_y[-1]]
          if rank == 4
          else [shape_x[-3], shape_x[-2], shape_y[-1]]
      )
      expected_output_type = tuple(out_shape) + (types.fp32,)
  
      def build(x, y):
          return mb.einsum(values=(x, y), equation=equation)
  
      if rank == 3:
          expected_output = np.einsum(
              equation,
              np.broadcast_to(
                  x_val, [shape_x[-3], shape_x[-2], shape_x[-1]]
              ),
              np.broadcast_to(
                  y_val, [shape_y[-3], shape_x[-2], shape_y[-1]]
              ),
          )
      else:
          expected_output = np.einsum(
              equation,
              np.broadcast_to(
                  x_val,
                  [shape_y[-4], shape_x[-3], shape_x[-2], shape_x[-1]],
              ),
              np.broadcast_to(
                  y_val,
                  [shape_y[-4], shape_y[-3], shape_x[-2], shape_y[-1]],
              ),
          )
  
      run_compare_builder(
          build,
          input_placeholder_dict,
          input_value_dict,
          expected_output_type,
          expected_output,
          compute_unit=compute_unit,
          backend=backend,
      )
  
imports: import itertools
  import platform
  import numpy
  import pytest
  import coremltools
  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.mil.types import builtin_to_string, nptype_from_builtin
  from coremltools.converters.mil.testing_reqs import compute_units
  from coremltools.converters.mil.testing_utils import random_gen, ssa_fn
label_desc: Use image node_id 2 for calling the TestEinsum obj's underlying member method code with example usage: obj.test_builder_to_backend_stress(compute_unit, rank, broadcast, backend) and returns: mb