of them that contain failures into one file. The TensorFlow DevInfra team
uses this to generate a simple overview of an entire pip and nonpip test
invocation, since the normal logs that Bazel creates are too large for the
internal invocation viewer.
"""
import collections
import os
import re
import subprocess
import sys
from junitparser import JUnitXml

result = JUnitXml()
try:

    mlir_components = "Bridge",
    tags = [
        "manual",
    ],
)

# Utility library for benchmark binaries, used by the *_benchmark rules that are
# added by the tfcompile bazel macro.
cc_library(
    name = "benchmark",
    srcs = ["benchmark.cc"],
    hdrs = ["benchmark.h"],
    visibility = ["//visibility:public"],
    deps = [

        Unlike the output of gen_test, this benchmark can be run on android.
      gen_compiler_log: If True, dumps XLA:CPU debug output to a log file.
      visibility: Bazel build visibility.
      testonly:   Bazel testonly attribute.
      tfcompile_flags: Extra flags to pass to tfcompile to control compilation.
      tfcompile_tool: The tfcompile binary. A non-default can be passed to

    graphdef = std::move(next);
  }

  TF_ASSIGN_OR_RETURN(string clustering_summary, SummarizeClustering(graphdef));

  // To update golden files flip this to true and run
  //
  // bazel test --test_strategy=local \
  //   tensorflow/compiler/jit/tests:auto_clustering_test
  bool update_golden = false;
  if (update_golden) {
    TF_RETURN_IF_ERROR(WriteStringToFile(

can be easily selected, incorporated, and reused in a wide range of
driver programs including command-line tools (such as vet), text editors and
IDEs, build and test systems (such as go build, Bazel, or Buck), test
frameworks, code review tools, code-base indexers (such as SourceGraph),
documentation viewers (such as godoc), batch pipelines for large code
bases, and so on.

# Analyzer

// during each invocation. The Go compiler conveniently accepts a
// -d=ignore=... debug flag that ignores its argument, so to run the
// previous example using Bazel, the invocation is:
//
//	bazel test --define=gc_goopts=-d=loopvarhash=PATTERN,unused=RANDOM //path/to:test
//
// [GODEBUG setting]: https://tip.golang.org/doc/godebug
package main

import (
	"context"
	"flag"
	"fmt"

        "node": ">=0.10.0"
      }
    },
    "node_modules/@babel/code-frame": {
      "version": "7.21.4",
      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.21.4.tgz",
      "integrity": "sha512-LYvhNKfwWSPpocw8GI7gpK2nq3HSDuEPC/uSYaALSJu9xjsalaaYFOq0Pwt5KmVqwEbZlDu81aLXwBOmD/Fv9g==",
      "dev": true,
      "dependencies": {
        "@babel/highlight": "^7.18.6"
      },
      "engines": {

    sdkmanager 'ndk;25.1.8937393'; \
    sdkmanager 'build-tools;27.0.3';

# Install Scoop and packages
RUN iex \"& {$(irm get.scoop.sh)} -RunAsAdmin\"; \
    scoop install perl; \
    scoop install bazel; \
    scoop install cuda; \
    scoop install azure-functions-core-tools; \
    scoop install azure-cli;

# Setting environment variables

section

## How to use

Once you have a converted TfLite model ready, you can use the following command
to use TAC to optimize for your model:

```
bazel run -c opt //tensorflow/compiler/mlir/lite/experimental/tac:tac-translate -- <PATH_TO_YOUR_MODEL> -o=<OUTPUT_PATH> -device-specs=<HARDWARE_BACKENDS>
```

The devices_specs is a list of the names of the desired hardware backends,

// block of comments in the file.
//
// readComments should be an internal detail,
// but widely used packages access it using linkname.
// Notable members of the hall of shame include:
//   - github.com/bazelbuild/bazel-gazelle
//
// Do not remove or change the type signature.
// See go.dev/issue/67401.
//
//go:linkname readComments
func readComments(f io.Reader) ([]byte, error) {
	r := newImportReader("", f)