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"""StableHLO Portable Python APIs.

This setup only exports the the StableHLO Portable C++ APIs, which have
signatures that do not rely on MLIR classes.

Exporting all of MLIR Python bindings to TF OSS has high maintenance
implications, especially given the frequency that TF updates the revision of
LLVM used.
"""

# pylint: disable=wildcard-import

relying on apocryphal or anecdotal advice.

[MinIO Commercial Licensing](https://min.io/pricing) is the best option for applications that trigger AGPLv3 obligations (e.g. open sourcing your application). Applications using MinIO - or any other OSS-licensed code - without validating their usage do so at their own risk....

import (
	"cmp"
	"go/token"
	"slices"
	"strconv"
)

// SortImports sorts runs of consecutive import lines in import blocks in f.
// It also removes duplicate imports when it is possible to do so without data loss.
func SortImports(fset *token.FileSet, f *File) {
	for _, d := range f.Decls {
		d, ok := d.(*GenDecl)
		if !ok || d.Tok != token.IMPORT {
			// Not an import declaration, so we're done.
			// Imports are always first.

load("//tensorflow:tensorflow.bzl", "tf_cc_test")
load(
    "//tensorflow/core/platform:build_config.bzl",
    "tf_proto_library",
)

# OSS only: This target is header-only. Link `debug_options_proto_cc_impl` only to
# `libtensorflow_framework.so` via `lib_internal_impl`. Do NOT link `debug_options_proto_cc_impl`
# directly unless the target does not link `libtensorflow_framework.so`.
tf_proto_library(
    name = "debug_options_proto",

 *     On OSs with hierarchical file system events (currently Windows and macOS) this is what we tell the
 *     operating system to watch.
 *     See {@link org.gradle.internal.watch.registry.impl.HierarchicalFileWatcherUpdater}.</dd>
 *
 *     <dt>watched directories</dt>
 *     <dd>On OSs with non-hierarchical file system events (currently Linux only) we don't watch whole

  not in each driver. The drivers then only have to deal with a
  smaller set of types.

* Be flexible with type conversions, but be paranoid about silent
  truncation or other loss of precision.

* Handle concurrency well.  Users shouldn't need to care about the
  database's per-connection thread safety issues (or lack thereof),
  and shouldn't have to maintain their own free pools of connections.

    // The upper bound on the length of an array and the bounds on the int values mean that, in
    // this case only, we can compute the sum as a long without risking overflow or loss of
    // precision. So we do that, as it's slightly quicker than the Knuth algorithm.
    long sum = 0;
    for (int index = 0; index < values.length; ++index) {
      sum += values[index];
    }

    // The upper bound on the length of an array and the bounds on the int values mean that, in
    // this case only, we can compute the sum as a long without risking overflow or loss of
    // precision. So we do that, as it's slightly quicker than the Knuth algorithm.
    long sum = 0;
    for (int index = 0; index < values.length; ++index) {
      sum += values[index];
    }

    python_version = "PY3",
    tags = [
        "no_cuda_asan",  # Not needed, and there were issues with timeouts.
        "no_oss",  # Avoid downloading mnist data set in oss.
        "nomultivm",  # Not needed. Save some resources and test time.
        "notap",  # The test is too long to run as part of llvm presubmits (b/173661843).
        "notpu",  # Takes too long (b/192305423)