}
        // value changed
      }
    }
  }

  /**
   * Copies all of the mappings from the specified map to this map. The effect of this call is
   * equivalent to that of calling {@code put(k, v)} on this map once for each mapping from key
   * {@code k} to value {@code v} in the specified map. The behavior of this operation is undefined
   * if the specified map is modified while the operation is in progress.

///

## Motivation { #motivation }

Let's start with a simple example:

{* ../../docs_src/python_types/tutorial001_py310.py *}

Calling this program outputs:

```
John Doe
```

The function does the following:

* Takes a `first_name` and `last_name`.
* Converts the first letter of each one to upper case with `title()`.

   *
   * @since 11.0
   */
  void put(K key, V value);

  /**
   * Copies all of the mappings from the specified map to the cache. The effect of this call is
   * equivalent to that of calling {@code put(k, v)} on this map once for each mapping from key
   * {@code k} to value {@code v} in the specified map. The behavior of this operation is undefined
   * if the specified map is modified while the operation is in progress.

      function = null;
    }

    /*
     * If set()/setValue() throws an Error, we let it propagate. Why? The most likely Error is a
     * StackOverflowError (from deep transform(..., directExecutor()) nesting), and calling
     * setException(stackOverflowError) would fail:
     *
     * - If the stack overflowed before set()/setValue() could even store the result in the output

        // Execute
        NtlmPasswordAuthentication result = ntlmSsp.doAuthentication(mockCifsContext, mockRequest, mockResponse, challenge);

        // Verify that the behavior is the same as calling the static method directly
        assertNull(result, "Authentication result should be null");
        verify(mockResponse).setHeader("WWW-Authenticate", "NTLM");

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

namespace tensorflow {

namespace {

// Managing context for the DLManagedTensor, will manage the lifetime of
// DLManagedTensor. When calling DLManagedTensor::deleter, it will notify the
// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;

    List<Object> result = doConcurrentGet(cache, "bar", count, startSignal);

    assertThat(callCount.get()).isEqualTo(1);
    for (int i = 0; i < count; i++) {
      // doConcurrentGet alternates between calling getUnchecked and calling get, but an unchecked
      // exception thrown by the loader is always wrapped as an UncheckedExecutionException.
      assertThat(result.get(i)).isInstanceOf(UncheckedExecutionException.class);

        auth.secureWipePassword();

        // Password should be null after wipe
        assertNull(auth.getPassword());
        assertNull(auth.getPasswordAsCharArray());

        // Calling wipe again should be safe
        auth.secureWipePassword();
        assertNull(auth.getPassword());
    }

    /**
     * Test that closed authenticator prevents all sensitive operations
     */
    @Test

        }
        return toReturn;
      }

      @GuardedBy("lock")
      /*
       * The GuardedBy checker warns us that we're not holding cancellationDelegate.lock. But in
       * fact we are holding it because it is the same as this.lock, which we know we are holding,
       * thanks to @GuardedBy above. (cancellationDelegate.lock is initialized to this.lock in the
       * call to `new SupplantableFuture` below.)
       */

    }

    @GwtIncompatible @J2ktIncompatible private static final long serialVersionUID = 0;
  }

  /**
   * Returns a supplier whose {@code get()} method synchronizes on {@code delegate} before calling
   * it, making it thread-safe.
   */
  @J2ktIncompatible
  public static <T extends @Nullable Object> Supplier<T> synchronizedSupplier(
      Supplier<T> delegate) {
    return new ThreadSafeSupplier<>(delegate);