{"copy", `var src, dst []byte; copy(dst, src)`, `func([]byte, []byte) int`},
	{"copy", `type T [][]int; var src, dst T; _ = copy(dst, src)`, `func(p.T, p.T) int`},
	{"copy", `var src string; var dst []byte; copy(dst, src)`, `func([]byte, string) int`},
	{"copy", `type T string; type U []byte; var src T; var dst U; copy(dst, src)`, `func(p.U, p.T) int`},

        "//tensorflow/compiler/mlir/tensorflow:__pkg__",
        "//tensorflow/compiler/mlir/tfrt:__subpackages__",
    ],
    licenses = ["notice"],
)

td_library(
    name = "tensorflow_tfrt_ops_td_files",
    srcs = [
        "tfrt_ops.td",
    ],
    compatible_with = get_compatible_with_portable(),
    deps = [
        "//tensorflow/compiler/mlir/tensorflow:tensorflow_ops_td_files",

// between). Otherwise the line is ignored.
func parseFlags(src []byte, flags *flag.FlagSet) error {
	// we must have a line comment that starts with a "-"
	const prefix = "//"
	if !bytes.HasPrefix(src, []byte(prefix)) {
		return nil // first line is not a line comment
	}
	src = src[len(prefix):]
	if i := bytes.Index(src, []byte("-")); i < 0 || len(bytes.TrimSpace(src[:i])) != 0 {
		return nil // comment doesn't start with a "-"
	}

  auto attr = node_def->mutable_attr();
  if (node_def->op() == "Cast" && attr->contains("SrcT") &&
      attr->contains("DstT") &&
      attr->at("SrcT").type() == attr->at("DstT").type() &&
      attr->contains("Truncate") && !attr->at("Truncate").b()) {
    node_def->set_op("Identity");
    attr->insert({{"T", attr->at("SrcT")}});
    attr->erase("SrcT");
    attr->erase("DstT");
    attr->erase("Truncate");
  }
}

    ],
    licenses = ["notice"],
)

package_group(
    name = "friends",
    packages = [
        "//tensorflow/compiler/mlir/...",
    ],
)

filegroup(
    name = "decomposition_lib",
    srcs = ["decomposition_lib.mlir"],
)

gen_op_bindings(name = "composite")

  }
  attr {
    key: "executor_type"
    value {
      s: ""
    }
  }
  attr {
    key: "f"
    value {
      func {
        name: "function"
      }
    }
  }
}
node {
  name: "spc2"
  op: "StatefulPartitionedCall"
  input: "input0"
  input: "args_0"
  attr {
    key: "Tin"
    value {
      list {
        type: DT_FLOAT
        type: DT_RESOURCE
      }
    }
  }
  attr {

    default_visibility = [
        ":internal_visibility_allowlist_package",
        "//tensorflow:__pkg__",
    ],
    licenses = ["notice"],
)

cc_library(
    name = "tf_to_stablehlo",
    srcs = [
        "tf_to_stablehlo.cc",
    ],
    hdrs = [
        "tf_to_stablehlo.h",
    ],
    compatible_with = get_compatible_with_portable(),
    deps = [

)

config_setting(
    name = "disable_mlir_config",
    flag_values = {":disable_mlir": "True"},
    visibility = ["//visibility:public"],
)

cc_library(
    name = "mlir",
    srcs = ["mlir.cc"],
    hdrs = ["mlir.h"],
    deps = [
        "@com_google_absl//absl/algorithm:container",
        "@com_google_absl//absl/container:flat_hash_set",
        "@com_google_absl//absl/container:inlined_vector",

)

# Run this binary manually, with an argument pointing to the testdata/
# directory, to generate the test files used by the filegroup rule below.
py_strict_binary(
    name = "gen_saved_models",
    srcs = ["gen_saved_models.py"],
    python_version = "PY3",
    deps = [
        "//tensorflow/python/compat:v2_compat",
        "//tensorflow/python/eager:def_function",
        "//tensorflow/python/framework:constant_op",

cc_library(
    name = "tensor_types_test_util",
    testonly = True,
    hdrs = ["tensor_types_test_util.h"],
    deps = [
        "//tensorflow/c:tf_datatype",
    ],
)

tf_cc_test(
    name = "tensor_test",
    srcs = [
        "tensor_test.cc",
    ],
    deps = [
        ":tensor_types_test_util",
        "//tensorflow/c:tf_datatype",
        "//tensorflow/cc/experimental/base/public:status",