</goals>
            <configuration>
              <bomClassifier>skinny</bomClassifier>
              <bomName>Maven Dependencies Skinny BOM</bomName>
              <bomDescription>Bill Of Materials for Apache Maven - Maven JARS only</bomDescription>
              <outputFilename>maven-skinny-bom.xml</outputFilename>
              <usePropertiesForVersion>true</usePropertiesForVersion>

**Note**: You can mix `def` and `async def` in your *path operation functions* as much as you need and define each one using the best option for you. FastAPI will do the right thing with them.

Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast.

But by following the steps above, it will be able to do some performance optimizations.

## Technical Details

    update your imports accordingly, the old files will be removed in Release
    2.11.
*   `tf.keras.optimizers.experimental.Optimizer` will graduate in Release 2.11,
    which means `tf.keras.optimizers.Optimizer` will be an alias of
    `tf.keras.optimizers.experimental.Optimizer`. The current
    `tf.keras.optimizers.Optimizer` will continue to be supported as
    `tf.keras.optimizers.legacy.Optimizer`,

				case io.EOF: // reader is exhausted
					return
				case nil: // no data and no error, retry fill
					goto scan
				default: // unexpected reader error
					sr.readerErr = err
					return
				}
			}

			rpos += int64(n)
			sr.fillReady <- int64(n)
		}
	}()

	sr.fillReq <- struct{}{} // initial fill

	return sr
}

// remaining returns the number of unread bytes

If you go to the **API docs UI** at `/docs` you will be able to see the **documentation** for cookies for your *path operations*.

But even if you **fill the data** and click "Execute", because the docs UI works with **JavaScript**, the cookies won't be sent, and you will see an **error** message as if you didn't write any values.

///

## Forbid Extra Cookies

# Note that numpy 2.1.0 does not support python 3.9
numpy >= 2.0.0, < 2.2.0
wheel ~= 0.41.2
h5py >= 3.11.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.4.0, < 0.5.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

# Requirements for NumPy 1.x
numpy ~= 1.26.0
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.4.0, < 0.5.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

 * acquire(1000)} will result in exactly the same throttling, if any), but it affects the throttling
 * of the <i>next</i> request. I.e., if an expensive task arrives at an idle RateLimiter, it will be
 * granted immediately, but it is the <i>next</i> request that will experience extra throttling,
 * thus paying for the cost of the expensive task.
 *

enum ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  DEVICE_PLACEMENT_WARN = 1,
  // Silently copy the tensor, which has a performance cost since the operation
  // will be blocked till the copy completes. This is the default policy.
  DEVICE_PLACEMENT_SILENT = 2,

  private void assertCleanup(
      LoadingCache<Integer, String> cache,
      CountingRemovalListener<Integer, String> removalListener) {

    // initialSize will most likely be 2, but it's possible for the GC to have already run, so we'll
    // observe a size of 1
    long initialSize = cache.size();
    assertTrue(initialSize == 1 || initialSize == 2);

    // wait up to 5s