func.eraseBody();
  func.addEntryBlock();
  ImplicitLocOpBuilder builder(func.getLoc(), func.getBody());
  llvm::SmallVector<int32_t> result_shape_i32;
  auto result_shape = result_ty.getShape();
  for (auto element : result_shape) {
    result_shape_i32.push_back(static_cast<int32_t>(element));
  }
  auto result_shape_attr = builder.getI32TensorAttr(result_shape_i32);
  Value result_shape_tensor =

    // TODO(hinsu): Handle constants that are not scalar booleans.
    auto cond_type = mlir::dyn_cast<RankedTensorType>(cond.getType());
    if (!cond_type || !cond_type.getShape().equals({}) ||
        !cond_type.getElementType().isInteger(/*width=*/1))
      return failure();

    // Identify the branch to inline.
    bool cond_value = (*cond.value_begin<APInt>()).getSExtValue();

  %shape1 = arith.constant dense<100> : tensor<2xi32>
  %r1 = "tf.Reshape" (%arg0, %shape1) : (tensor<*xf32>, tensor<2xi32>) -> tensor<100x100xf32>
  %shape2 = "tf.Shape"(%arg0) : (tensor<*xf32>) -> tensor<?xi32>
  %r2 = "tf.Reshape"(%arg1, %shape2) : (tensor<*xf32>, tensor<?xi32>) -> tensor<*xf32>
  %r3 = "tf.Reshape"(%arg2, %shape1) : (tensor<10000xf32>, tensor<2xi32>) -> tensor<100x100xf32>

absl::StatusOr<arith::ConstantOp> CreateConstOpWithVectorValue(
    PatternRewriter* rewriter, Location loc, ShapedType shaped_type,
    int value) {
  ShapedType dense_type = RankedTensorType::get(shaped_type.getShape(),
                                                shaped_type.getElementType());
  auto attr = CreateTypedAttr(dense_type, value);

  return rewriter->create<arith::ConstantOp>(loc, dense_type,

      %1 = stablehlo.get_dimension_size %0, dim = 0 : (tensor<?x3xf32>) -> tensor<i32>
      %2 = stablehlo.reshape %1 : (tensor<i32>) -> tensor<1xi32>
      %3 = stablehlo.concatenate %2, %cst_0, dim = 0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>

    %3 = stablehlo.add %arg2, %0 : tensor<100xi64>
    %4 = stablehlo.select %2, %3, %arg2 : tensor<100xi1>, tensor<100xi64>
    %5 = stablehlo.reshape %4 : (tensor<100xi64>) -> tensor<100x1xi64>
    %6 = "stablehlo.scatter"(%arg0, %5, %arg3) ({
    ^bb0(%arg5: tensor<f32>, %arg6: tensor<f32>):
      stablehlo.return %arg6 : tensor<f32>

  %4 = "tfl.reshape"(%3, %cst) : (tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>, tensor<2xi32>) -> tensor<1x401408x!quant.uniform<u8:f32, 0.023528476789885875>>

};
std::unique_ptr<TargetHardwareOperation> CreateArithmeticOp() {
  return std::make_unique<CpuArithmeticOp>();
}

// Currently used for these ops:
// tfl.concatenation / tfl.reshape / tfl.pack
class CpuConcatOp : public TargetHardwareOperation {
  double GetOpCost(mlir::Operation* op) const override {
    float cost = 0.0;
    int64_t count;

  // equal.
  TensorType GetTransposedTensorType(
      const TensorType type, const ArrayRef<int64_t> permutation) const {
    const SmallVector<int64_t> after_shape =
        Permute<int64_t>(type.getShape(), permutation);
    return type.cloneWith(after_shape, type.getElementType());
  }
};

}  // namespace

void NchwConvolutionToNhwcPass::runOnOperation() {
  func::FuncOp func_op = getOperation();

  TF_SetStatus(status, TF_OK, "");
  auto* cc_ctx = reinterpret_cast<InferenceContext*>(ctx);
  auto* cc_result = reinterpret_cast<ShapeHandle*>(result);
  Status s = cc_ctx->Subshape(*reinterpret_cast<ShapeHandle*>(shape_handle),
                              start, end, cc_result);
  Set_TF_Status_from_Status(status, s);
}

int64_t TF_DimensionHandleValue(TF_DimensionHandle* dim_handle) {