%read1 = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
      %var_handle = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      %read2 = "tf.ReadVariableOp"(%var_handle) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>

// CHECK: %[[VAR_HANDLE:.*]], %[[CTL_1:.*]] = tf_executor.island wraps "tf.VarHandleOp"() <{{{.*shared_name = "var_0".*}}}>
// CHECK: %[[RESTORE:.*]], %[[CTL_2:.*]] = tf_executor.island wraps "tf.RestoreV2"(%[[ARG]], %[[CONST_1]], %[[CONST_0]])
// CHECK: %[[CTL_3:.*]] = tf_executor.island wraps "tf.AssignVariableOp"(%[[VAR_HANDLE]], %[[RESTORE]])

// CHECK-NEXT: return %[[q]]
}

// CHECK-LABEL: NotQuantizeReadVariable
func.func @NotQuantizeReadVariable() -> tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>> {
  %0 = "tfl.var_handle"() {container = "", shared_name = "states"} : () -> tensor<!tf_type.resource<tensor<1x2x3xf32>>>
  %1 = "tfl.read_variable"(%0) : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32>

// CHECK-DAG: %[[VAR_HANDLE_0:.*]] = "tf.VarHandleOp"() {{.*shared_name = "const_0".*}}
// CHECK-DAG: "tf.AssignVariableOp"(%[[VAR_HANDLE_0]], %[[CST_0]])

// CHECK-DAG: %[[CST_1:.*]] = "tf.Const"() <{{{.*value = dense<2.000000e\+00> : tensor<8xf32>.*}}}>
// CHECK-DAG: %[[VAR_HANDLE_1:.*]] = "tf.VarHandleOp"()  {{.*shared_name = "const_1".*}}
// CHECK-DAG: "tf.AssignVariableOp"(%[[VAR_HANDLE_1]], %[[CST_1]])

  IMAG=133,
  REAL=134,
  COMPLEX_ABS=135,
  HASHTABLE = 136,
  HASHTABLE_FIND = 137,
  HASHTABLE_IMPORT = 138,
  HASHTABLE_SIZE = 139,
  REDUCE_ALL = 140,
  CONV_3D_TRANSPOSE = 141,
  VAR_HANDLE = 142,
  READ_VARIABLE = 143,
  ASSIGN_VARIABLE = 144,
}
// LINT.ThenChange(nnapi_linter/linter.proto)

// Options for the builtin operators.
union BuiltinOptions {
  Conv2DOptions,

  const string dev2_name = "/job:def/replica:0/task:0/device:CPU:0";
  TFE_TensorHandle* var_handle2 = TestVariable(ctx, 2.0, dev2_name);
  EXPECT_NE(var_handle2, nullptr);
  EXPECT_EQ(tensorflow::unwrap(var_handle2)->DeviceName(&status2), dev2_name);

  TFE_DeleteTensorHandle(var_handle0);
  TFE_DeleteTensorHandle(var_handle1);
  TFE_DeleteTensorHandle(var_handle2);

  TFE_DeleteContext(ctx);
  TF_DeleteStatus(status);

  IMAG=133,
  REAL=134,
  COMPLEX_ABS=135,
  HASHTABLE = 136,
  HASHTABLE_FIND = 137,
  HASHTABLE_IMPORT = 138,
  HASHTABLE_SIZE = 139,
  REDUCE_ALL = 140,
  CONV_3D_TRANSPOSE = 141,
  VAR_HANDLE = 142,
  READ_VARIABLE = 143,
  ASSIGN_VARIABLE = 144,
  BROADCAST_ARGS = 145,
  RANDOM_STANDARD_NORMAL = 146,
  BUCKETIZE = 147,
  RANDOM_UNIFORM = 148,
  MULTINOMIAL = 149,
  GELU = 150,

  }
};

// Checks if the input producer op is supported in this transform. Right now, we
// only check if it is a tf.IteratorGetNext where resource input is coming from
// a VarHandle on CPU or a function argument assigned to CPU.
bool IsSupportedInputOp(
    Operation* op,
    const TF::ResourceAliasAnalysis::Info& resource_alias_analysis) {

    because the tensors are loaded asynchronously.

  }];

  let arguments = (ins
    TFTensorType:$prefix,
    TFTensorType:$tensor_names,
    TFTensorType:$shape_and_slices,
    Variadic<TFTensorType>:$var_handles,
    TypeArrayAttr: $restored_dtypes,
    DenseBoolArrayAttr: $truncate_in_cast
  );
}

    if (variables.count(GetResourceKey(var_handle_op)))
      work_list.push_back(var_handle_op);
  }

  // Capture list of ops to be erased by traversing usage starting from
  // the VarHandle ops.
  llvm::SetVector<Operation*> erase_list;
  while (!work_list.empty()) {
    auto* operation = work_list.pop_back_val();
    erase_list.insert(operation);
    for (auto& use : operation->getUses()) {