// CHECK-DAG: %[[CST:.*]] = "tf.Const"
// CHECK-DAG: %[[VAR_HANDLE:.*]] = "tf.VarHandleOp"
// CHECK: "tf.AssignVariableOp"(%[[VAR_HANDLE]], %[[CST]])

// Test that a new save function that wraps the SaveV2 op is created.
// CHECK: func.func private @tf_quant__save(%[[ARG:.*]]: tensor<!tf_type.string>)
// CHECK: %[[VAR_HANDLE:.*]] = "tf.VarHandleOp"()
// CHECK-SAME: {{.*shared_name = "var_0".*}}

// CHECK: tf_executor.graph
// CHECK: %[[VAR_HANDLE:.*]], {{.*}} = tf_executor.island wraps "tf.VarHandleOp"() <{{{.*shared_name = "var_0".*}}}>
// CHECK: %[[READ_VARIABLE:.*]], {{.*}} = tf_executor.island wraps "tf.ReadVariableOp"(%[[VAR_HANDLE]])

Empty strings indicate that they are non-partitioned tensors.}]>:$shape_and_slices,
    // VarHandles for restored tensors.
    Variadic<TF_Tensor>:$var_handles,
    // Data types for restored tensor.
    TypeArrayAttr: $restored_dtypes,
    // whether trucate is used when casting from the restored_dytpe to the type specified in VarHandle
    DenseBoolArrayAttr: $truncate_in_cast
  );

}

func.func @resource_output_gets_control_node() -> tensor<!tf_type.resource<tensor<1xf32>>> {
  %var = "tfl.var_handle"() {container = "", shared_name = "states"} : () -> tensor<!tf_type.resource<tensor<1xf32>>>
  func.return %var : tensor<!tf_type.resource<tensor<1xf32>>>
}
// CHECK-NEXT: %[[VAR:.*]], %[[CONTROL:.*]] = tfl.control_node controls "tfl.var_handle"
// CHECK-NEXT: return %[[VAR]]


// CHECK-LABEL: @tfl_call_once_gets_control_node

                                  strlen(ResourceHandle::ANONYMOUS_NAME)));

  AbstractTensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TF_RETURN_IF_ERROR(varhandle_op->Execute(
      absl::MakeSpan(&var_handle, num_retvals), &num_retvals));
  AbstractTensorHandlePtr owned_var_handle(var_handle);
  if (!tensorflow::isa<ImmediateExecutionTensorHandle>(
          owned_var_handle.get())) {

    func.return %2, %3 : tensor<1x10xf32>, tensor<1x10xi64>
  }

  // CHECK: %[[RESOURCE:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "a"}> : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>
  // CHECK: %[[RESOURCE_1:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "b"}> : () -> tensor<!tf_type.resource<tensor<1x10xi64>>>

//
// It converts (VariableV2 -> Identity) to (VarHandle -> ReadVariable).
//
// For the background, this pass is a part of hoisting VariableV2 ops by
// re-using the pipeline for hoisting (VarHandle -> ReadVariable) cases, which
//  can be done by the following passes:
//  - Capturing resource values into global tensors (importing saved model).
//  - Promoting VarHandle ops to function input/outputs.
//  - Freezing global tensor pass.

#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "tensorflow/core/framework/shape_inference.h"

namespace tensorflow {
namespace {

// Implementation of varhandle that binds a VarHandleOp to an XlaResource of the
// same name. It is not safe to use this op in a JIT context.
class XlaAotOnlyVarHandleOp : public XlaOpKernel {
 public:

  let summary = "Lower tf.ReadVariable and tf.AssignVariable";

  let description = [{
    This pass will lower AssignVariableOp and ReadVariableOp to their ml_program
    equivalents, for those resources which come from a VarHandle op, but also
    for those which come from a "bound input" parameter. For the latter,
    said parameter will not be removed, unlike LowerGlobalsToMlProgramPass.

    So this will lower e.g.

    // simple like "cast" and keep tracing.
    return traceUpwardsToArgument(v.getDefiningOp()->getOperand(0), seen, out);
  } else {
    // Typically a tf.VarHandle op.
    return v.getDefiningOp()->emitOpError("Non constant predecessor");
  }
}

// For the "resource" attribute in a ReadVariable or AssignVariable op,