hdrs = ["quantize_model.h"],
    compatible_with = get_compatible_with_portable(),
    visibility = [
        # Directly linked to `libtensorflow_cc.so` or
        # `_pywrap_tensorflow_internal.so` if static build.
        "//tensorflow:__pkg__",
        "//tensorflow/python:__pkg__",
    ],
    deps = [
        ":py_function_lib",
        ":unfreeze_constants",
        "//tensorflow/cc/saved_model:loader",

Arturo Schmidt <******@****.***> 1718227424 -0700

Sandeep Dasgupta <******@****.***> 1716330452 -0700

Sandeep Dasgupta <******@****.***> 1716330452 -0700

A. Unique TensorFlower <******@****.***> 1718085734 -0700

Arturo Schmidt <******@****.***> 1718227424 -0700

	testenv.MustHaveGoBuild(t)
	dir := t.TempDir()
	if fixedTestDir {
		dir = "/tmp/qqqzzz"
		os.RemoveAll(dir)
		mkdir(t, dir)
	}

	// Build harness. We need two copies of the harness, one built
	// with -covermode=atomic and one built non-atomic.
	bdir1 := mkdir(t, filepath.Join(dir, "build1"))
	hargs1 := []string{"-covermode=atomic", "-coverpkg=all"}
	atomicHarnessPath := buildHarness(t, bdir1, hargs1)
	nonAtomicMode := testing.CoverMode()

Luke Boyer <******@****.***> 1716935812 -0700

        ),
    ],
    tblgen = "@llvm-project//mlir:mlir-tblgen",
    td_file = "ir/tf_ops.td",
    deps = [
        ":tensorflow_ops_td_files",
    ],
)

# We only shard tf_op on name for build performance reasons.
tf_ops_category_list = [
    {
        "name": "ops_a_m",
        "include": "tf.[A-M].*$",
    },
    {
        "name": "ops_n_z",
        "include": "tf.[N-Z].*$",
    },
]

Arturo Schmidt <******@****.***> 1718227424 -0700