* `datetime.date`:
    * Python `datetime.date`.
    * In requests and responses will be represented as a `str` in ISO 8601 format, like: `2008-09-15`.
* `datetime.time`:
    * A Python `datetime.time`.
    * In requests and responses will be represented as a `str` in ISO 8601 format, like: `14:23:55.003`.
* `datetime.timedelta`:

      out.append(charSequence);
    }
  }

  /**
   * Writes the given lines of text to this sink with each line (including the last) terminated with
   * the operating system's default line separator. This method is equivalent to {@code
   * writeLines(lines, System.getProperty("line.separator"))}.
   *
   * @throws IOException if an I/O error occurs while writing to this sink
   */

                if (!line.startsWith("#")) {
                    final String urlValue;
                    if (urlEncodeDisabled.get()) {
                        urlValue = line;
                        urlEncodeDisabled.set(false);
                    } else {
                        urlValue = systemHelper.encodeUrlFilter(line);
                    }

	for input.Scan() {
		line := strings.TrimSpace(input.Text())
		if line == "" {
			continue
		}
		// Find first separator.
		idx := strings.IndexByte(line, trailerKVSeparator[0])
		if idx <= 0 || idx >= len(line) {
			if cr.debug {
				fmt.Printf("Could not find separator, got %q\n", line)
			}
			return errMalformedEncoding
		}
		key := strings.ToLower(line[:idx])
		value := line[idx+1:]

---
name: TensorFlow Lite in Play Services issue
about: Use this template for issues with TensorFlow Lite in Google Play Services
labels: 'comp:lite-in-play-services'

---

**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
  if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version

	var files []string
	for _, line := range lines {
		if len(line) == 0 {
			continue
		}
		xmlLines := getLines(parseMobileProvision(string(line)))
		matches := 0
		for _, udid := range udids {
			for _, xmlLine := range xmlLines {
				if bytes.Contains(xmlLine, udid) {
					matches++
				}
			}
		}
		if matches == len(udids) {
			files = append(files, string(line))
		}
	}
	return files
}

        "//tensorflow/lite/c:common",
        "//tensorflow/lite/core/api",
        "//tensorflow/lite/delegates/flex:delegate",
        "//tensorflow/lite/kernels/internal:compatibility",
        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/kernels:reference_ops",
        "//tensorflow/compiler/mlir/lite/schema:schema_fbs",
        "//tensorflow/lite/toco/logging:conversion_log_util",

    }

    public void test_constructor_withBufferSizeAndCallback() throws IOException {
        Process mockProcess = createMockProcess("callback test\nanother line");
        List<String> callbackResults = new ArrayList<>();
        Consumer<String> callback = line -> callbackResults.add(line);

        JobProcess jobProcess = new JobProcess(mockProcess, 50, callback);

        assertNotNull(jobProcess);

2)  Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).

```

For example, you could use it to read and verify passwords generated by another system (like Django) but hash any new passwords with a different algorithm like Bcrypt.

And be compatible with all of them at the same time.

///

Create a utility function to hash a password coming from the user.