} else if (auto call_op = llvm::dyn_cast<TF::PartitionedCallOp>(&op)) {
      auto callee = call_op.func();
      if (!callee) {
        return call_op.emitOpError(
            "resource lifting does not support call with nested references.");
      }
      if (failed(HandlePartitionedCallOp(call_op, callee, module,
                                         vars_initialized,

  func::FuncOp main_fn = FindMainFuncOp(*module_op);
  ASSERT_THAT(main_fn, NotNull());

  Operation* call_op = FindOperationOfType<TF::XlaCallModuleOp>(main_fn);
  EXPECT_FALSE(IsHybridQuantizedOp(call_op));
}

constexpr absl::string_view kModuleDotGeneralFullyConnected = R"mlir(
  module {

      [&reachable_funcs](Region& src,
                         SmallVectorImpl<func::FuncOp>& funcs_to_visit) {
        src.walk([&reachable_funcs, &funcs_to_visit](CallOpInterface call_op) {
          auto func = dyn_cast_or_null<func::FuncOp>(call_op.resolveCallable());
          if (func && reachable_funcs.insert(func).second)
            funcs_to_visit.push_back(func);
        });
      };

  /** Generate report on tracked memory (old macro). */
  #define CU_DUMP_MEMORY_USAGE(x) CU_dump_memory_usage((x))
#else   /* MEMTRACE */
  /** Standard calloc() if MEMTRACE not defined. */
  #define CU_CALLOC(x, y)         calloc((x), (y))
  /** Standard malloc() if MEMTRACE not defined. */
  #define CU_MALLOC(x)            malloc((x))
  /** Standard free() if MEMTRACE not defined. */

// Copyright 2022 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package calloc

// This package contains a simple "batch" allocator for allocating
// coverage counters (slices of uint32 basically), for working with
// coverage data files. Collections of counter arrays tend to all be
// live/dead over the same time period, so a good fit for batch
// allocation.

    // CHECK:     name: "foo"
    // CHECK:   }
    // CHECK: }
    %0 = tf_executor.island wraps "tf.PartitionedCall"() {Tin = [], Tout = [], config = "", config_proto = "", device = "", executor_type = "", f = @foo, name = "Call_foo"} : () -> ()
    tf_executor.fetch
  }
  func.return
}

// CHECK:      library {
// CHECK:        function {
// CHECK-NEXT:     signature {
// CHECK-NEXT:       name: "bar"
// CHECK-NEXT:     }

  // CHECK:          name: "foo"
  // CHECK:        }
    %1:2 = tf_executor.island wraps "tf.PartitionedCall"(%0) {Tin = [], Tout = [], config = "", config_proto = "", device = "", executor_type = "", f = @foo, name = "Call_foo"} : (tensor<f32>) -> tensor<*xf32>
    tf_executor.fetch
  }
  func.return
}

// CHECK:      library {
// CHECK-NEXT:   function {
// CHECK-NEXT:     signature {
// CHECK-NEXT:       name: "foo"

// than function name.
std::unique_ptr<OpQuantSpec> GetTFOpQuantSpec(Operation* op) {
  auto spec = std::make_unique<OpQuantSpec>();
  if (auto call_op = dyn_cast<TF::PartitionedCallOp>(op)) {
    StringRef function_name =
        mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue();
    if (!function_name.starts_with("composite_")) {
      return spec;
    }
    if (function_name.contains("depthwise_conv2d")) {

  TF::YieldOp yield =
      llvm::dyn_cast_or_null<TF::YieldOp>(block.getTerminator());
  if (!yield) return std::nullopt;
  func::CallOp call = llvm::dyn_cast_or_null<func::CallOp>(*block.begin());
  if (!call) return std::nullopt;
  if (block.getNumArguments() != call.getNumOperands() ||
      call.getNumResults() != yield.getNumOperands())
    return std::nullopt;

    if (!main_func) continue;

    SymbolTable symbol_table(module_op);
    for (auto call_op : main_func.getOps<TF::PartitionedCallOp>()) {
      func_ops.push_back(dyn_cast_or_null<func::FuncOp>(symbol_table.lookup(
          mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue())));
    }
    for (auto call_op : main_func.getOps<TF::StatefulPartitionedCallOp>()) {
      func_ops.push_back(