EXPECT_TRUE(params_->body_graph != nullptr);
  EXPECT_TRUE(params_->cond_graph != nullptr);

  EXPECT_EQ(params_->ninputs, 2);

  ASSERT_TRUE(params_->cond_inputs != nullptr);
  ASSERT_TRUE(params_->cond_inputs[0].oper != nullptr);
  EXPECT_TRUE(params_->cond_inputs[1].oper != nullptr);

  ASSERT_TRUE(params_->body_inputs != nullptr);
  EXPECT_TRUE(params_->body_inputs[0].oper != nullptr);

// WhileOp
//===----------------------------------------------------------------------===//

static LogicalResult VerifyWhileTypes(Operation *op, TypeRange cond_input,
                                      TypeRange body_input,
                                      TypeRange body_result,
                                      bool shape_invariant) {

    if (!CreateInput(inputs[i], cond_graph, StrCat("cond_input", i).c_str(),
                     &cond_inputs[i], status)) {
      break;
    }
    if (!CreateInput(inputs[i], body_graph, StrCat("body_input", i).c_str(),
                     &body_inputs[i], status)) {
      break;
    }
  }

  TF_WhileParams params = {ninputs,    cond_graph,  cond_inputs,  cond_output,

  // expected-error@+1 {{'tf_saved_model.bound_input' attribute should be a FlatSymbolRefAttr}}
  func.func @f(
    %arg0: tensor<f32> {tf_saved_model.bound_input = 1 : i32}
  ) attributes { tf_saved_model.exported_names = ["foo.some_func"] } {
    func.return
  }

}

// -----

module attributes {tf_saved_model.semantics} {

  func.func @f(%arg1: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @"v"}, %arg2: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @"v2"})
  attributes {tf_saved_model.exported_names = ["f"]} {
    // CHECK-DAG: "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}>

  // CHECK: func @h(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @c})
  func.func @h(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @c})
  attributes {tf_saved_model.exported_names = ["h"]} {
    %0 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
    func.return
  }

        if (func.getArgAttrOfType<FlatSymbolRefAttr>(i, bound_input)) {
          func.removeArgAttr(i, bound_input);
        }
        func.removeArgAttr(i, index_path);
      }
      for (unsigned i = 0, e = func.getNumResults(); i != e; ++i) {
        func.removeResultAttr(i, bound_input);
        func.removeResultAttr(i, index_path);
      }
    });

  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<?xf32>, value = dense<1.> : tensor<1xf32> } : () -> ()
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<?xf32>>> {tf_saved_model.bound_input = @v})
  -> (tensor<?xf32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %0 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<?xf32>>>) -> tensor<?xf32>

  // CHECK: func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v})
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v}) -> (tensor<f32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["f"]} {

  // CHECK-LABEL: @read_from_global
  func.func @read_from_global(%arg0: tensor<!tf_type.resource<tensor<10xf32>>> {tf_saved_model.bound_input = @v})
  attributes {tf_saved_model.exported_names = ["read_from_global"]} {
    // CHECK: [[name:%.*]] = "tf.VarHandleOp"
    // CHECK: "tf.ReadVariableOp"([[name]])