std::istreambuf_iterator<char>()};
  }

  flatbuffers::Verifier model_verifier(
      reinterpret_cast<const uint8_t*>(serialized_model->c_str()),
      serialized_model->length());
  if (!model_verifier.VerifyBuffer<Model>()) {
    std::cerr << "Verification failed.\n";
    return true;
  }
  return false;
}

	}) {
		t.Errorf("got %v, wanted {'Test': 12u, 'Check': 34u}", mpStrUint)
	}
	tstStr, err := mv.ConvertToNative(reflect.TypeOf(&tstStruct{}))
	if err != nil {
		t.Fatal(err)
	}
	if !reflect.DeepEqual(tstStr, &tstStruct{
		Test:  uint64(12),
		Check: uint64(34),
	}) {
		t.Errorf("got %v, wanted tstStruct{Test: 12u, Check: 34u}", tstStr)
	}
}

func TestMapValueEqual(t *testing.T) {
	mv := NewMapValue()

					"rpc": {
						{source: "db"},
						{source: "files"},
					},
					"netgroup": {
						{source: "nis"},
					},
				},
			},
		},
	}

	for _, tt := range tests {
		gotConf := nssStr(t, tt.in)
		gotConf.mtime = time.Time{} // ignore mtime in comparison
		if !reflect.DeepEqual(gotConf, tt.want) {
			t.Errorf("%s: mismatch\n got %#v\nwant %#v", tt.name, gotConf, tt.want)
		}
	}

//===----------------------------------------------------------------------===//
// String/variant/resource types (including corresponding reference types)

def TF_Str : AnyTypeOf<
  [TF_TensorFlowType<"String", "str">, TF_StrRef], "string">;
def TF_StrTensor : TensorOf<[TF_Str]>;

def TF_Variant : AnyTypeOf<
  [TF_TensorFlowType<"Variant", "var">, TF_VariantRef], "variant">;
def TF_VariantTensor : TensorOf<[TF_Variant]>;

		fields := strings.Fields(line)
		if len(fields) != 12 {
			return funcInlHeur, nil, fmt.Errorf("malformed callsite (nf=%d) %s line %d: %s", len(fields), dr.p, dr.ln, line)
		}
		if fields[2] != "flagstr" || fields[4] != "flagval" || fields[6] != "score" || fields[8] != "mask" || fields[10] != "maskstr" {

  auto module_op = module.get();
  auto serialized_result_fb = ExportRuntimeMetadata(module_op);
  const auto* result = GetRuntimeMetadata(serialized_result_fb.value().c_str());
  const auto* expected = GetRuntimeMetadata(kExpectedFB.c_str());
  ASSERT_TRUE(result != nullptr);
  ASSERT_TRUE(result->subgraph_metadata() != nullptr);
  ASSERT_TRUE(expected->subgraph_metadata() != nullptr);
  Verify(result, expected);
}

      TF_NewStatus(), TF_DeleteStatus);

  // Start a new function / execution context.
  string fn_name = "double";
  TF_ExecutionContext* graph_ctx =
      TF_CreateFunction(fn_name.c_str(), status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  auto* placeholder_t =
      TF_AddFunctionParameter(graph_ctx, TF_FLOAT, {-1, nullptr}, status.get());

 private:
  // Whether to run this pass. If this is enabled, the NodeDef will be imported
  // to MLIR even no tf composition file is found.
  bool IsEnabled() {
    const char* tfr_lib_env_val = getenv(string(kTFRLibEnv).c_str());
    return tfr_lib_env_val != nullptr;
  }
};

}  // namespace tfr
}  // namespace tensorflow

    std::string flops_str;
    std::string mac_str;
    if (ops_count < 10000) {
      flops_str = absl::StrFormat("%ld ", ops_count);
      mac_str = absl::StrFormat("%ld ", ops_count / 2);
    } else if (ops_count < billion) {
      flops_str =
          absl::StrFormat("%.3f M ", static_cast<double>(ops_count) / million);
      mac_str = absl::StrFormat("%.3f M ",

}  // namespace
}  // namespace mlir

int main(int argc, char** argv) {
  llvm::InitLLVM y(argc, argv);
  llvm::cl::ParseCommandLineOptions(argc, argv);
  auto file_or_err = llvm::MemoryBuffer::getFileOrSTDIN(inputFileName.c_str());
  if (std::error_code error = file_or_err.getError()) {
    llvm::errs() << argv[0] << ": could not open input file '" << inputFileName
                 << "': " << error.message() << "\n";
    return 1;
  }