virtual void ClearCachesAndThreadExecutors() = 0;

  // Initialize the step resource container for a training step. This is used
  // in current TF runtime. For tfrt, it is used by fallback op handler.
  virtual void StartStep() = 0;
  // Destroy the step resource container for a training step.
  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager

     * the root URL <code>smb://</code> is also <code>smb://</code>. If this
     * <tt>SmbFile</tt> refers to a workgroup, server, share, or directory,
     * the name will include a trailing slash '/' so that composing new
     * <tt>SmbFile</tt>s will maintain the trailing slash requirement.
     *
     * @return The last component of the URL associated with this SMB
     *         resource or <code>smb://</code> if the resource is <code>smb://</code>

            // buffer, then append it with a preceding space...again, not sure what else to
            // do other than collapse whitespace.
            // NOTE: The case of a trailing quote is handled by nullifying the arg buffer.
            if (!addedToBuffer) {
                if (currentArg != null) {
                    currentArg.append(' ').append(arg);
                } else {

  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));

  TF_DeleteExecutionContext(graph_ctx);
}

// The above tests are run for a combination of:
// - graphdef and MLIR tracing engine
INSTANTIATE_TEST_SUITE_P(Tracing, UnifiedCAPI,
                         ::testing::Combine(::testing::Values("graphdef",
                                                              "mlir"),

import java.io.ObjectInputStream;
import java.io.Serializable;
import javax.annotation.CheckForNull;

/**
 * List returned by {@link ImmutableCollection#asList} that delegates {@code contains} checks to the
 * backing collection.
 *
 * @author Jared Levy
 * @author Louis Wasserman
 */
@GwtCompatible(serializable = true, emulated = true)
@SuppressWarnings("serial")
@ElementTypesAreNonnullByDefault

/**
 * A listening executor service which forwards all its method calls to another listening executor
 * service. Subclasses should override one or more methods to modify the behavior of the backing
 * executor service as desired per the <a
 * href="http://en.wikipedia.org/wiki/Decorator_pattern">decorator pattern</a>.
 *
 * <p><b>{@code default} method warning:</b> This class does <i>not</i> forward calls to {@code

import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A list multimap which forwards all its method calls to another list multimap. Subclasses should
 * override one or more methods to modify the behavior of the backing multimap as desired per the <a
 * href="http://en.wikipedia.org/wiki/Decorator_pattern">decorator pattern</a>.
 *
 * <p><b>{@code default} method warning:</b> This class does <i>not</i> forward calls to {@code

/**
 * A sorted set multimap which forwards all its method calls to another sorted set multimap.
 * Subclasses should override one or more methods to modify the behavior of the backing multimap as
 * desired per the <a href="http://en.wikipedia.org/wiki/Decorator_pattern">decorator pattern</a>.
 *
 * <p><b>{@code default} method warning:</b> This class does <i>not</i> forward calls to {@code

import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A {@link ListenableFuture} which forwards all its method calls to another future. Subclasses
 * should override one or more methods to modify the behavior of the backing future as desired per
 * the <a href="http://en.wikipedia.org/wiki/Decorator_pattern">decorator pattern</a>.
 *
 * <p>Most subclasses can just use {@link SimpleForwardingListenableFuture}.
 *
 * @author Shardul Deo

to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.