jcifs-ng will be a proper choice for many users. 
There are a lot of SMB devices in the world.
Some of them only work with the old jcifs library.
If you want to support many SMB devices, CodeLibs jcifs library will be helpful.
For example, since [Fess](https://github.com/codelibs/fess) needs to support many SMB devices, it uses this library.

---
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

    srcs = ["c_api_experimental_test.cc"],
    data = [
        "testdata/tf_record",
        "//tensorflow/c/experimental/stream_executor/test:test_pluggable_device.so",
        "//tensorflow/core/common_runtime/next_pluggable_device/c:test_next_pluggable_device_plugin.so",
    ],
    extra_copts = if_google(["-DTENSORFLOW_NO_SHARED_OBJECTS=1"]),
    linkopts = select({
        "//tensorflow:macos": ["-headerpad_max_install_names"],

## Why distributed MinIO?

MinIO in distributed mode can help you setup a highly-available storage system with a single object storage deployment. With distributed MinIO, you can optimally use storage devices, irrespective of their location in a network.

### Data protection

 * class to read a response that is larger than the entire memory allocated to the current process.
 * It can even stream a response larger than the total storage on the current device, which is a
 * common requirement for video streaming applications.
 *
 * Because this class does not buffer the full response in memory, the application may not

        "//tensorflow/core:test_main",
        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "custom_device_testutil",
    testonly = True,
    srcs = ["custom_device_testutil.cc"],
    hdrs = ["custom_device_testutil.h"],
    visibility = ["//tensorflow:internal"],
    deps = [
        ":c_api",
        ":c_api_experimental",
        ":c_api_test_util",

import okhttp3.internal.readFieldOrNull

/**
 * Base Android reflection based SocketAdapter for the built in Android SSLSocket.
 *
 * It's assumed to always be present with known class names on Android devices, so we build
 * optimistically via [buildIfSupported].  But it also doesn't assume a compile time API.
 */
class StandardAndroidSocketAdapter(
  sslSocketClass: Class<in SSLSocket>,

*   The following APIs are not longer experimental:
    `tf.config.list_logical_devices`, `tf.config.list_physical_devices`,
    `tf.config.get_visible_devices`, `tf.config.set_visible_devices`,
    `tf.config.get_logical_device_configuration`,
    `tf.config.set_logical_device_configuration`.
*   `tf.config.experimentalVirtualDeviceConfiguration` has been renamed to

Android Test
============

A gradle module for running Android instrumentation tests on a device or emulator.

1. Add an Emulator named `pixel5`, if you don't already have one

```
$ sdkmanager --install "system-images;android-29;google_apis;x86"
$ echo "no" | avdmanager --verbose create avd --force --name "pixel5" --device "pixel" --package "system-images;android-29;google_apis;x86" --tag "google_apis" --abi "x86"
```

)

# Config setting that is satisfied when CUDA device code should be compiled
# with clang. It does not imply that CUDA support has been enabled.
alias(
    name = "is_cuda_compiler_clang",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_compiler_clang",
        "@local_config_cuda//cuda:TRUE",
    ),
)

# Config setting that is satisfied when CUDA device code should be compiled