#      Ex. linux_x86        -- x86_64 Linux platform
#      Ex. linux_x86_cuda   -- x86_64 Linux platform, with Nvidia CUDA support
#      Ex. macos_arm64      -- arm64 MacOS platform
# 3. Add modifiers. Some modifiers for local execution are:
#      Ex. disk_cache -- Use a local cache
#      Ex. public_cache -- Use TF's public cache (read-only)
#      Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only)

 * acquisition order.
 *
 * <p>Potential deadlocks detected when calling the {@code lock()}, {@code lockInterruptibly()}, or
 * {@code tryLock()} methods will result in the execution of the {@link Policy} specified when
 * creating the factory. The currently available policies are:
 *
 * <ul>
 *   <li>DISABLED
 *   <li>WARN
 *   <li>THROW
 * </ul>
 *

        if (lookup == null) {
            return customLifecycles != null ? customLifecycles : new HashMap<>();
        }

        // Lifecycles cannot be cached as extensions might add custom lifecycles later in the execution.
        try {
            return registry != null
                    ? registry.stream().collect(Collectors.toMap(lf -> lf.id(), lf -> new Lifecycle(registry, lf)))
                    : Map.of();

// The panic built-in function stops normal execution of the current
// goroutine. When a function F calls panic, normal execution of F stops
// immediately. Any functions whose execution was deferred by F are run in
// the usual way, and then F returns to its caller. To the caller G, the
// invocation of F then behaves like a call to panic, terminating G's
// execution and running any deferred functions. This continues until all

#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Abstract interface to an operation.
// This interface allows building and executing an operation in either
// tracing or immediate execution mode.
class AbstractOperation {
 protected:
  enum AbstractOperationKind {
    kGraph,
    kMlir,
    kEager,
    kTfrt,
    kTape,
    kOpHandler
  };

void TF_SetTracingImplementation(const char* name, TF_Status* s) {
  tsl::Set_TF_Status_from_Status(s, SetDefaultTracingEngine(name));
}

// Creates a new TensorFlow function, it is an execution context attached to a
// given tracing context.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* s) {
  return wrap(CreateTracingExecutionContext(fn_name, s));
}

  AbstractTensorHandle* ZerosLike() const;

  AbstractTensorHandle* GetHandle() const;

 private:
  AbstractTensorHandle* handle_;
};

// A tracing/immediate-execution agnostic tape.
//
// Gradient functions defined for this tape must support handling null incoming
// gradients.
class Tape : protected eager::GradientTape<AbstractTensorHandle,

    server.enqueue(mockResponse.build())

    client.newCall(request).execute().use {
      it.body.bytes()
    }
    return client.newCall(request).execute()
  }

  private operator fun get(url: HttpUrl): Response {
    val request =
      Request.Builder()
        .url(url)
        .build()
    return client.newCall(request).execute()
  }

  private fun writeFile(
    directory: Path,

// Test to ensure that a registered graph optimization pass is only executed
// once (i.e., on the main function side) in running distributed functions.
// This test creates a cluster with two workers, create a variable on the
// second worker, and run a distributed function (VariableAddFunction) whose ops
// span the local and remote workers. If the graph optimization pass is executed

        <executions>
          <execution>
            <id>test</id>
            <goals>
              <goal>test</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-assembly-plugin</artifactId>
        <executions>
          <execution>
            <id>create-distro-packages</id>