// CHECK: %[[ADD:.*]] = "tf.Add"(%[[MUL]], %[[MUL]]) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    // CHECK: return %[[ADD]] : tensor<1xf32>
    %0 = "tf.Mul"(%arg0, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
    %1 = "tf.Add"(%0, %0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %1 : tensor<*xf32>
  }

  // CHECK-LABEL: func @simple_chain_with_broadcast

  StatusOr<TaggedValue> v = tf_function.Execute(ctx_.get(), args);
  ASSERT_TRUE(v.ok()) << v.status().message();
  const TaggedValue& result = v.value();
  ExpectEquals(result.tuple()[0].tensor().get(), 2.0f);
  ExpectEquals(result.tuple()[1].tensor().get(), 4.0f);
}

TEST_P(FunctionTest, UnaryFuncCalledWithMultipleArgsFails) {
  // Construct a scalar.
  impl::TaggedValueTensor x = CreateScalarTensor<float, TF_FLOAT>(2.0f);

          name: "tensor_names"
          type: DT_STRING
        }
        input_arg {
          name: "shape_and_slices"
          type: DT_STRING
        }
        output_arg {
          name: "tensors"
          type_list_attr: "dtypes"
        }
        attr {
          name: "dtypes"
          type: "list(type)"
          has_minimum: true
          minimum: 1
        }
      }
      op {

                             Tensor* tensor) override TF_LOCKS_EXCLUDED(mu_);

  Status MakeTensorFromProto(DeviceContext* device_context,
                             const TensorProto& tensor_proto,
                             const AllocatorAttributes alloc_attrs,
                             Tensor* tensor);

  const Metadata& metadata() { return xla_metadata_; }

      const int input_rank = input_type.getRank();

      // Create a 1D I32 tensor for representing the dimension permutation.
      auto permuation_tensor_type =
          RankedTensorType::get({input_rank}, rewriter.getIntegerType(32));
      llvm::SmallVector<Attribute, 4> permute;
      permute.reserve(input_rank);
      // First create an identity permutation tensor.
      for (int i = 0; i < input_rank; i++) {

        correspond to.
      tags: Set of tags associated with the model.
      inputs: Input name -> input tensor mapping.
      outputs: Output name -> output tensor mapping.
      init_op: Op for initialization.
      assets_collection: Assets collection. This collection is a list of string
        tensors. Each tensor contains the asset file names.
    """
    v1_builder = builder.SavedModelBuilder(saved_model_path)

                             const QuantizationSpecs& quantization_specs,
                             const DebuggerConfig& debugger_config) {
  // Convert NCHW tensors to NHWC at along with extra optimizations as
  // downstream passes perform better optimizations when dealing with NHWC
  // formatted tensors.
  AddProcessNchwTensorPasses(pm);

  pm.addPass(CreateLiftQuantizableSpotsAsFunctionsPass(quantization_specs));
  if (debugger_config.debugger_type() !=

    new (&v.data_.dict) T(std::make_shared<T::element_type>());
    return v;
  }
  /// Constructs a TaggedValue with type TENSOR.
  static TaggedValue Tensor(tensorflow::AbstractTensorHandle* raw_ptr) {
    TaggedValue v;
    v.type_ = TENSOR;
    using T = decltype(v.data_.tensor);
    new (&v.data_.tensor) T(raw_ptr, /*add_ref=*/false);
    return v;
  }

      CHECK-LABEL: tfr.func @tf__test_identity_op(%x: !tfr.tensor) -> (!tfr.tensor) {
      CHECK-NEXT:   %[[raw_data:.*]] = tfr.quant_raw_data(%x) : (!tfr.tensor) -> (!tfr.tensor)
      CHECK-NEXT:   %[[qparam:.*]]:2 = tfr.quant_qparam(%x) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
      CHECK:        %[[list:.*]] = "tfr.build_list"(%[[qparam]]#0, %[[qparam]]#0) : (!tfr.tensor, !tfr.tensor) -> !tfr.tensor_list

// A pass that creates a new function that wraps the newly created SaveV2 op.
// The new function's name is "tf_quant__save". The function accepts a single
// string tensor as argument, which specifies the path to the checkpoint to
// which the variable's tensor values are saved. It finds
// `tf.AssignVariableOp(tf.VarHandleOp, tf.Const)` pattern in the initializer
// function of type "restore_op" to identify the VarHandleOps that should be