code: def _check(self, s, *input_shapes, **kwargs):
      dtype = kwargs.pop("dtype", np.float32)
      r = np.random.RandomState(0)
      inputs = []
      for shape in input_shapes:
          with self.subTest(s=s, shape=shape):
              arr = np.array(r.randn(*shape)).astype(dtype)
              if dtype == np.complex64 or dtype == np.complex128:
                  arr += 1j * np.array(r.randn(*shape)).astype(dtype)
              inputs.append(arr)
      input_tensors = [
          constant_op.constant(x, shape=x.shape) for x in inputs
      ]
      a = np.einsum(s, *inputs)
      b = self.evaluate(gen_linalg_ops.einsum(input_tensors, s))
      self.assertAllClose(a, b, atol=1e-4, rtol=1e-4)
  
imports: import numpy
  from tensorflow.python.client import session
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_linalg_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import special_math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import benchmark
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the EinsumOpTest obj's underlying member method code with example usage: obj._check(s) without return types