EXPECT_EQ(revived_tensor->Type(), expected.dtype());
  EXPECT_EQ(revived_tensor->NumElements(), expected.NumElements());
  EXPECT_EQ(revived_tensor->NumDims(), expected.dims());
  for (int i = 0; i < expected.dims(); ++i) {
    EXPECT_EQ(revived_tensor->Dim(i), expected.dim_size(i));
  }

  testing::CheckBufferDataIsEqual(expected.dtype(), expected.NumElements(),
                                  revived_tensor->Data(), expected.data());

      assert(index[i] < shape[i]);
      assert(index[i] >= 0);
    }
#endif  // NDEBUG
    for (int64_t dim = shape.size() - 1; dim >= 0; --dim) {
      if (dim == fixed_axis) continue;
      ++index[dim];
      if (index[dim] < shape[dim]) return std::move(index);
      index[dim] = 0;
    }
    return std::nullopt;
  }

 protected:

  shape->reserve(input_shape.dims());
  for (const auto& d : input_shape) {
    shape->push_back(d.size == kTFDynamicSize ? ShapedType::kDynamic : d.size);
  }
}

Status ConvertToMlirShape(const TensorShapeProto& input_shape,
                          llvm::SmallVectorImpl<int64_t>* shape) {
  shape->reserve(input_shape.dim_size());
  auto& dims = input_shape.dim();
  for (auto& d : dims) {

    const int64_t rank = type.getRank();
    for (const int64_t dim : batch_dimensions) {
      batch_dimensions_.axes.push_back(dim);
      batch_dimensions_.sizes.push_back(type.getDimSize(dim));
    }

    for (const int64_t dim : contracting_dimensions) {
      contracting_dimensions_.axes.push_back(dim);
      contracting_dimensions_.sizes.push_back(type.getDimSize(dim));
    }

      // the same because all strides are 1).
      low_padding[dim] =
          conv_grad_spatial_dim.pad_before - orig_padding[dim].first;
      high_padding[dim] =
          conv_grad_spatial_dim.pad_after - orig_padding[dim].second;

      // Update `out_grad_shape` to result shape of following `PadOp`.
      out_grad_shape[dim] = low_padding[dim] + high_padding[dim] +

    std::vector<DimensionHandle> dims;
    const TF_ShapeAndType& input_shape = input_shapes->items[i];
    if (input_shape.num_dims == InferenceContext::kUnknownRank) {
      c.SetInput(i, c.UnknownShape());
      continue;
    }
    dims.reserve(input_shape.num_dims);
    for (int j = 0; j < input_shape.num_dims; ++j) {
      dims.push_back(c.MakeDim(input_shape.dims[j]));
    }
    c.SetInput(i, c.MakeShape(dims));

    %3 = shape.shape_of %2 : tensor<?x2xf32> -> tensor<2xindex>
    %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [1] : (tensor<2xf32>, tensor<2xindex>) -> tensor<?x2xf32>
    %5 = stablehlo.add %2, %4 : tensor<?x2xf32>
    %6 = stablehlo.clamp %0, %5, %1 : (tensor<f32>, tensor<?x2xf32>, tensor<f32>) -> tensor<?x2xf32>

  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }
  TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                   sizeof(TF_TString) * num_elements);
  delete[] dims;

      // $element_shape, $max_num_elements
      CompileTimeConstantOperand<TF::EmptyTensorListOp, 0, 1>,
      CompileTimeConstantOperand<TF::ExpandDimsOp, 1>,   // $dim
      CompileTimeConstantOperand<TF::FillOp, 0>,         // $dims
      CompileTimeConstantOperand<TF::GatherV2Op, 2>,     // $axis
      CompileTimeConstantOperand<TF::IRFFT2DOp, 1>,      // $fft_length

        def dir1 = tmpDir.createDir("dir1")
        def dir2 = dir1.createDir("dir2")

        expect:
        def set = from(dir2, dir1)
        set.contains(dir1)
        set.contains(dir2)
        set.contains(dir1.file("child"))
        set.contains(dir2.file("child"))
        !set.contains(dir1.parentFile)
        !set.contains(tmpDir.file("dir"))