if (node->type_string() == "Const" && must_be_true) {
      const TensorProto* proto = nullptr;
      TF_RETURN_IF_ERROR(GetNodeAttr(node->def(), "value", &proto));

      Tensor tensor(proto->dtype());
      TF_RET_CHECK(tensor.FromProto(*proto));

      *predicate = tensor.scalar<bool>()() ? MakeTrue() : MakeFalse();
      return absl::OkStatus();
    }

  TF_OperationDescription* init(string type) {
    // Construct op_name to match the name used by REGISTER_OP in the
    // ATTR_TEST_REGISTER calls above.
    string op_name = "CApiAttributesTestOp";
    if (type.find("list(") == 0) {
      op_name += "List";
      type = type.replace(0, 5, "");
      type = type.replace(type.size() - 1, 1, "");
    }
    op_name += type;
    return TF_NewOperation(

  if (!x_type) llvm_unreachable("unsupported type");
  Type type = x_type.clone(builder->getI32Type());
  return builder->create<CastOp>(loc, type, x, truncate);
}

static APFloat ConvertToAPFloat(double val, Type type) {
  if (type.getIntOrFloatBitWidth() == 32) {
    return APFloat(static_cast<float>(val));
  }

  return APFloat(val);
}

    save.save(
        model,
        self._input_saved_model_path,
        signatures=model.matmul.get_concrete_function(
            tensor_spec.TensorSpec(
                shape=input_shape, dtype=dtypes.float32, name='input_tensor'
            )
        ),
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(50):

// Test cluster with bad `num_cores_per_replica` attribute.
!rtype = tensor<!tf_type.resource<tensor<10x3xf32>>>
func.func @replication_with_model_parallelism(%arg0: !rtype, %arg1: !rtype, %arg2: !rtype, %arg3: !rtype) -> (tensor<10x3xf32>) {
  %2 = "tf.TPUReplicatedInput"(%arg0, %arg2) : (!rtype, !rtype) -> !rtype
  %3 = "tf.TPUReplicatedInput"(%arg1, %arg3) : (!rtype, !rtype) -> !rtype

#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

// Specification for expected input/output and its type.
// DataType value of DT_INVALID signifies that we don't want to
// check the data type.
typedef std::pair<string, DataType> IOSpec;

const char* kFeedStackToString = "File \"feed.cc\", line 10, in alpha";

}

// demangleType parses:
//
//	<type> ::= <builtin-type>
//	       ::= <function-type>
//	       ::= <class-enum-type>
//	       ::= <array-type>
//	       ::= <pointer-to-member-type>
//	       ::= <template-param>
//	       ::= <template-template-param> <template-args>
//	       ::= <substitution>
//	       ::= <CV-qualifiers> <type>
//	       ::= P <type>
//	       ::= R <type>

// This should be used in the private: declarations for a class.
#define GTEST_DISALLOW_ASSIGN_(type)\
  void operator=(type const &)

// A macro to disallow copy constructor and operator=
// This should be used in the private: declarations for a class.
#define GTEST_DISALLOW_COPY_AND_ASSIGN_(type)\
  type(type const &);\
  GTEST_DISALLOW_ASSIGN_(type)

// Tell the compiler to warn about unused return values for functions declared

// This should be used in the private: declarations for a class.
#define GTEST_DISALLOW_ASSIGN_(type)\
  void operator=(type const &)

// A macro to disallow copy constructor and operator=
// This should be used in the private: declarations for a class.
#define GTEST_DISALLOW_COPY_AND_ASSIGN_(type)\
  type(type const &);\
  GTEST_DISALLOW_ASSIGN_(type)

// Tell the compiler to warn about unused return values for functions declared

====

We are using the Gradle API to refer to the tasks by name and type, rather than using accessors.
Note that it's necessary to specify the type of the task explicitly, otherwise the script won't compile because the inferred type will be `Task`, not `Test`, and the `testLogging` property is specific to the `Test` task type.