namespace {

TEST(ResourceVariableGradTest, ReadVariableOpGrad) {
  TensorShape shape({});
  auto scope = Scope::NewRootScope();
  auto x = Placeholder(scope, DT_FLOAT, Placeholder::Shape(shape));

  auto var = VarHandleOp(scope, DT_FLOAT, shape);
  auto init = AssignVariableOp(scope, var, Const(scope, 2.0f, shape));

  auto temp = ReadVariableOp(scope, var, DT_FLOAT);

  auto y = Mul(scope, temp, x);

      "IdentityN",  // Variadic input+output
      "SparseSoftmaxCrossEntropyWithLogits",  // 2 outputs
      "AccumulatorApplyGradient",             // 0 outputs
      "VarHandleOp",                          // type, shape, list(string) attrs
      "RestoreV2",  // Variadic output-only, list(type) attr
  };

  cpp::CppConfig cpp_config(category, name_space);

  bool unconditionally_use_set_output_shapes = false;

  // Apply default attributes from the op definition to the loaded op.
  bool add_default_attributes = true;

  // If set, promote tf.VarHandleOp to resource arguments for all functions.
  bool lift_variables = true;

  // Keeps the variables in initializers before lifting variables (when
  // `lift_variables == true`) or newly adding variable initialization patterns

  auto* operation = resource.getDefiningOp();
  if (operation && isa<TF::VarHandleOp>(operation)) {
    mutable_variables_.insert(GetResourceKey(operation));
  }
}

bool ResourceAnalyzer::IsPotentiallyWritten(Value resource) const {
  assert(IsResource(resource));
  auto* operation = resource.getDefiningOp();
  if (operation && isa<TF::VarHandleOp>(operation))
    return mutable_variables_.contains(GetResourceKey(operation));

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"

namespace tensorflow {
namespace ops {

// Creates a handle to a Variable resource.
Status VarHandleOp(AbstractContext* ctx, AbstractTensorHandle** resource,
                   DataType dtype, const PartialTensorShape shape,
                   const char* container = "", const char* shared_name = "",

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace internal {

// Executes a VarHandleOp using `ctx`, and fills `handle` with the DT_RESOURCE
// TensorHandle associated with the variable. This is equivalent to creating an
// unitialized TF2 tf.Variable.

      Operation *op = *latticeElement->getValue().ops.begin();
      GlobalTensorOp globalTensor = llvm::dyn_cast<GlobalTensorOp>(op);

      if (!globalTensor)
        continue;  // happens if the name is e.g. in a VarHandleOp.

      if (globalTensor.getIsMutable()) {
        freezeable[val] = false;
        continue;
      }

      freezeable[val] = true;

      // Verify users are supported kind.

    type = op->getResult(0).getType().cast<ShapedType>();
  } else if (llvm::isa<TF::AssignVariableOp>(op)) {
    type = op->getOperand(1).getType().cast<ShapedType>();
  } else if (llvm::isa<TF::VarHandleOp>(op)) {
    type = op->getResult(0)
               .getType()
               .cast<ShapedType>()
               .getElementType()
               .cast<TF::TensorFlowTypeWithSubtype>()
               .GetSubtypes()

//

  static const char* kOpsToSkip[] = {
      "IdentityOp",
      "NoOp",              // b/174596063
      "OptionalHasValue",  // b/173136483
      "OptionalGetValue",  // b/173136483
      "VarHandleOp",       // b/176819198
  };
  for (const char* skip : kOpsToSkip) {
    if (absl::StartsWith(orig_op->op_name(), skip)) return absl::OkStatus();
  }

  tf_core_op_expansion_node_counter->GetCell()->IncrementBy(1);