code: def test_binary_tensors(self):
      # Testing using Metatensors
      def add(x, y):
          return x + y
  
      def div(x, y):
          return x / y
  
      functions = [add, div]
  
      input_shapes = [
          ((1, 1, 2), (1, 2)),  # Different Dim, non-zerodim
          ((), (1, 2)),  # One zerodim
          ((1, 2), ()),  # Other zerodim
          ((2, 0, 3), (1, 3)),  # Test a tensor with a dim of 0
          ((), ()),  # both zerodim
      ]
  
      input_dtypes = [
          (float32, float32),  # Simple Case
          (
              int32,
              int64,
          ),  # Size Promotion (compliated case for 0dim tensors)
          (float32, int32),  # type Promotion
          (int64, float32),  # Type promotion with size change
          (
              float64,
              complex32,
          ),  # Show we can handle complex vals as well
      ]
  
      for fn, in_shapes, in_dtypes in product(
          functions, input_shapes, input_dtypes
      ):
          self.assert_dtype_equal(fn, in_shapes, in_dtypes)
  
imports: from itertools import product
  from typing import Tuple
  from unittest.case import expectedFailure
  import torch
  from torch import complex32, float32, float64, int32, int64
  from torch.jit._passes import _property_propagation
  from torch.testing._internal.common_methods_invocations import SampleInput, sample_inputs_adaptive_avg_pool2d, sample_inputs_conv2d
  from torch.testing._internal.common_utils import set_default_dtype, first_sample
  from torch.testing._internal.jit_utils import JitTestCase
  from torch.testing._internal.jit_metaprogramming_utils import create_traced_fn
  from torch.testing._internal.common_device_type import ops, instantiate_device_type_tests
  from torch.testing._internal.common_methods_invocations import op_db
label_desc: Use image node_id 2 for calling the TestDtypeAnalysis obj's underlying member method code with example usage: obj.test_binary_tensors() and returns: unknown, unknown