LLVM_DEBUG(llvm::dbgs() << "Zero point value should be a tensor type. Got: "
                            << zero_points_type << ".\n");
    return failure();
  }

  if (Type zero_points_element_type = zero_points_type.getElementType();
      !mlir::isa<IntegerType>(zero_points_element_type)) {
    LLVM_DEBUG(llvm::dbgs() << "Zero point should be an integer type. Got: "
                            << zero_points_element_type << ".\n");

    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";

static const char id[] = "\n@(#)$Id: testregex (AT&T Research) 2010-06-10 $\0\n";

#if _PACKAGE_ast
#include <ast.h>
#else
#include <sys/types.h>
#endif

#include <stdio.h>
#include <regex.h>
#include <ctype.h>
#include <setjmp.h>
#include <signal.h>
#include <string.h>
#include <unistd.h>

#ifdef	__STDC__
#include <stdlib.h>
#include <locale.h>
#endif

#ifndef RE_DUP_MAX
#define RE_DUP_MAX	32767

}

// 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