// CHECK: %[[VAR_VAL:.*]] = "tfl.read_variable"(%[[RESOURCE]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>) -> tensor<1x10xf32>
  // CHECK: %[[ADD:.*]] = tfl.add %[[VAR_VAL]], %arg0 {fused_activation_function = "NONE"} : tensor<1x10xf32>
  // CHECK: "tfl.assign_variable"(%[[RESOURCE]], %[[ADD]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> ()

// CHECK-SAME: {{.*shared_name = "var_1".*}}

// ReadVariableOps are inserted for each VarHandleOp to read the tensor values.
// CHECK-DAG: %[[READ_VARIABLE_0:.*]] = "tf.ReadVariableOp"(%[[VAR_HANDLE_0]]) : (tensor<!tf_type.resource<tensor<2xf32>>>) -> tensor<2xf32>
// CHECK-DAG: %[[READ_VARIABLE_1:.*]] = "tf.ReadVariableOp"(%[[VAR_HANDLE_1]]) : (tensor<!tf_type.resource<tensor<3xf32>>>) -> tensor<3xf32>

// 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]])
// CHECK-DAG: %[[CST_0:.*]], {{.*}} = tf_executor.island wraps "tf.Const"() <{{{.*value = dense<"var_0"> : tensor<1x!tf_type\.string>.*}}}>

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

// Verify that attribute T is not set as ref type.
// CHECK-LABEL: name: "Ref_Variable"
// CHECK-LABEL: name: "foo"
// CHECK:      attr {
// CHECK-NEXT:   key: "T"
// CHECK-NEXT:   value {
// CHECK-NEXT:     type: DT_INT32{{$}}
// CHECK-NEXT:   }
// CHECK-NEXT: }

func.func @main() {
  tf_executor.graph {

  %val1 = "tf.Identity"(%val0) {_class = ["loc:@v"]} : (tensor<96x!tf_type.f32ref>) -> tensor<96xf32>
  func.return
}

// -----

// Test case: Two ReadVariable ops.

func.func @f() {
  // CHECK: "tf.VarHandleOp"

  // During lowering to resource variables, this pass will preserve the
  // locations of the ReadVariableOps as Identity ops to keep the original graph

// resource variables.
//
// 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).

  let summary = "Freeze tf_saved_model.global_tensor's in func bodies.";

  let description = [{
    This pass will replace a func's bound inputs which are bound to
    tf.ReadVariable ops global tensors with tf.Const ops inside the func's body.
    If this pass runs successfully, the resultant IR will be guaranteed to:

    1. Not contain any tf_saved_model.global_tensor ops

// Executes a ReadVariableOp using `ctx`. This reads the underlying variable
// value of `variable_handle` and copies the value to `output`. `dtype` must be
// the dtype of the variable associated with `variable_handle`.
Status ReadVariable(ImmediateExecutionContext* ctx,
                    ImmediateExecutionTensorHandle* variable_handle,
                    DataType dtype, ImmediateTensorHandlePtr* output);

    %1:2 = tf_executor.island wraps "tf.VariableV2"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>, shape = #tf_type.shape<0>, container = "", shared_name = ""} : () -> tensor<!tf_type.int32ref> loc("Ref_Variable")
    %2:2 = tf_executor.Enter %1#0 frame "while/while_context" parallel_iterations 10 : (tensor<!tf_type.int32ref>) -> (tensor<*x!tf_type.int32ref>, !tf_executor.control) {device = "", T = "tfdtype$DT_INT32"} loc("while/Enter")

  TF_RETURN_IF_ERROR(assign_op->AddInput(value));

  int num_retvals = 0;
  TF_RETURN_IF_ERROR(assign_op->Execute({}, &num_retvals));
  return Status();
}

Status ReadVariable(ImmediateExecutionContext* ctx,
                    ImmediateExecutionTensorHandle* variable_handle,
                    DataType dtype, ImmediateTensorHandlePtr* output) {
  ImmediateOpPtr read_op(ctx->CreateOperation());