// 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,

  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()) {

// RUN: tf-tfrt-opt -split-input-file -tfrt-lower-tf-savedmodel=hoist-invariant-ops=true %s | FileCheck %s --dump-input=fail --dump-input-filter=all

module attributes {tf_saved_model.semantics} {

// Test hoisting varhandle op.

// CHECK-LABEL: func @_tfrt_resource_init
// CHECK: [[handle:%.*]] = "tf.VarHandleOp"() <{container = "", shared_name = "x"}> : () -> tensor<!tf_type.resource<tensor<i32>>>