// 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>.*}}}>

// 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);

  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());

// RUN: tf-opt %s -tf-tpu-colocate-composite-resource-ops | FileCheck %s

// Tests ReadVariable op using composite device resource is wrapped inside
// tf_device.Cluster.

// CHECK-LABEL: func @testReadVariableOpColocated
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
func.func @testReadVariableOpColocated(%arg0: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
  // CHECK:      tf_device.replicate

  bool shape_inference = true;
  // Whether to do TFLite runtime verification.
  bool runtime_verification = true;
  // Whether to enable TFLite variables or not, this will allow
  // mutable variables and produce ReadVariable/AssignVariable ops in TFLite.
  bool enable_tflite_variables = false;
  // Whether to disable the variable freezing pass or not.
  // By default we freeze all variables and disallow mutable variables. When

  } else {
    // Typically a tf.VarHandle op.
    return v.getDefiningOp()->emitOpError("Non constant predecessor");
  }
}

// For the "resource" attribute in a ReadVariable or AssignVariable op,
// determine the symbol reference to the (new) ml_program::GlobalOp.
SymbolRefAttr lookupGlobalTensor(func::FuncOp func, Value resource,
                                 SymbolTable &syms,