</mxCell>
                <mxCell id="32" value="" style="pointerEvents=1;shadow=0;dashed=0;html=1;fillColor=#505050;labelPosition=center;verticalLabelPosition=bottom;verticalAlign=top;outlineConnect=0;align=center;shape=mxgraph.office.devices.laptop;strokeColor=none;" parent="33" vertex="1">
                    <mxGeometry width="500" height="350" as="geometry"/>
                </mxCell>

        `max_batch_size` for building TensorRT engines. Add parameters
        `use_dynamic_shape` to enable dynamic shape support. The default is to
        disable dynamic shape support. Add `dynamic_shape_profile_strategy` for
        selecting a dynamic shape profile strategy. The default is profile
        strategy is `Range`.
    *   Issue a warning when function get_tensorrt_rewriter_config is used.

  (`Settings` > `Apps` > `Google Play Services` > `App details`):

**Standalone code to reproduce the issue**
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to or attach code demonstrating
the problem.

**Any other info / logs**
Include any logs or source code that would be helpful to diagnose the problem.

Addresses specify a webserver (like `github.com`) and all of the **static** configuration necessary to connect to that server: the port number, HTTPS settings, and preferred network protocols (like HTTP/2).

    val request2 = Request.Builder().url(server.url("/")).build()
    val response2 = client.newCall(request2).execute()
    assertThat(response2.body.string()).isEqualTo("def")

    // The HTTP calls should share a single connection.
    assertThat(socksProxy.connectionCount()).isEqualTo(1)
  }

  @Test
  fun proxySelector() {
    server.enqueue(MockResponse.Builder().body("abc").build())
    val proxySelector: ProxySelector =

    ],
)

cc_library(
    name = "tf_shape",
    srcs = ["tf_shape.cc"],
    hdrs = ["tf_shape.h"],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros",
        ":tf_shape_internal",
        "//tensorflow/core:framework",
    ],
)

cc_library(
    name = "tf_shape_internal",
    hdrs = ["tf_shape_internal.h"],
    copts = tf_copts(),

This is very useful when you need to:

* Have shared logic (the same code logic again and again).
* Share database connections.
* Enforce security, authentication, role requirements, etc.
* And many other things...

All these, while minimizing code repetition.

## First Steps

 *
 * The task runner is also responsible for releasing held threads when the library is unloaded.
 * This is for the benefit of container environments that implement code unloading.
 *
 * Most applications should share a process-wide [TaskRunner] and use queues for per-client work.
 */
class TaskRunner(
  val backend: Backend,
  internal val logger: Logger = TaskRunner.logger,
) {
  val lock: ReentrantLock = ReentrantLock()

# export TFCI=py311,linux_x86,no_docker

# Advanced: Use Remote Build Execution (RBE) (internal developers only)
#
#   RBE dramatically speeds up builds and testing. It also gives you a
#   public URL to share your build results with collaborators. However,
#   it is only available to a limited set of internal TensorFlow developers.
#
#   RBE is incompatible with local caching, so you must remove

**NOTE:**