code: def test_shape_function_includes(self):
      inp_shape = [1, 16, 5, 10]
      weight_shape = [33, 16, 3, 3]
      bias = None
      stride = [2, 2]
      padding = [0, 0]
      dilation = [1, 1]
      groups = 1
      res = torch.jit._shapes.conv2d(
          inp_shape,
          weight_shape,
          bias,
          stride,
          padding,
          dilation,
          groups,
      )
      self.assertEqual(res, [1, 33, 2, 4])
  
      m1_shape = [10, 20]
      m2_shape = [20, 10]
      res = torch.jit._shapes.matmul(m1_shape, m2_shape)
      self.assertEqual(res, [10, 10])
  
imports: import operator
  import unittest
  from textwrap import dedent
  import torch
  from torch import nn, Tensor
  from torch.testing import FileCheck
  from torch.testing._internal.common_methods_invocations import sample_inputs_cat_concat
  from torch.testing._internal.common_utils import make_tensor
  from torch.testing._internal.jit_utils import JitTestCase, execWrapper
  from typing import List, Any
label_desc: Use image node_id 27 for calling the TestSymbolicShapeAnalysis obj's underlying member method code with example usage: obj.test_shape_function_includes() without return types