V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

    style V2 fill:#f9fff3
    style V1 fill:#fff6f0
    style V1Field fill:#fff6f0
    style V2Field fill:#f9fff3
```

...but, you can have separated models using Pydantic v1 and v2 in the same app.

```mermaid
graph TB
    subgraph "✅ Supported"
        direction TB

 * whenever there is a chance the check may fail "in real life". Example:
 *
 * {@snippet :
 * Bill bill = remoteService.getLastUnpaidBill();
 *
 * // In case bug 12345 happens again we'd rather just die
 * Verify.verify(bill.status() == Status.UNPAID,
 *     "Unexpected bill status: %s", bill.status());
 * }
 *
 * <h3>Comparison to alternatives</h3>
 *

        auth.resetAuthenticationTimestamp();
        long afterReset = System.currentTimeMillis();

        // Should still not be expired
        assertFalse(auth.isExpired());

        // Wait for original TTL to pass
        Thread.sleep(600);

        // Should still not be expired because we reset
        assertFalse(auth.isExpired());

        // Wait for the full TTL since reset

numpy>=2.2.6 ; python_version == "3.14"
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0 ; python_version <= "3.13"
h5py ~= 3.15.1 ; python_version == "3.14"
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.4, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras

        assertSame(b, reused, "Buffer should be reused from cache");
        // And it should still be zeroed
        for (byte value : reused) {
            assertEquals(0, value, "Reused buffer must be zeroed");
        }
    }

    // Edge: cache size 0 means nothing is cached; release still zeroes argument
    @Test
    @DisplayName("Cache size 0: release zeroes but does not cache; getBuffer allocates new")

**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 { #technical-details }

   * values are null. This method may update {@code alternatingKeysAndValues} if there are duplicate
   * keys. If so, the return value will indicate how many entries are still valid, and will also
   * include a {@link Builder.DuplicateKey} in case duplicate keys are not allowed now or will not
   * be allowed on a later {@link Builder#buildOrThrow()} call.
   *

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

    }
  }

  override fun handshake(): Handshake? = handshake

  /** Track a bad route in the route database. Other routes will be attempted first. */
  internal fun connectFailed(
    client: OkHttpClient,
    failedRoute: Route,
    failure: IOException,
  ) {
    // Tell the proxy selector when we fail to connect on a fresh connection.
    if (failedRoute.proxy.type() != Proxy.Type.DIRECT) {

        buffer[bufferIndex] = 0x00;
        buffer[bufferIndex + 1] = 0x00;
        buffer[bufferIndex + 2] = 0x00;
        SMBUtil.writeInt4(1500, buffer, bufferIndex + 3);

        // Fill rest of required fields
        for (int i = 7; i < 37; i++) {
            buffer[bufferIndex + i] = 0x00;
        }

        response.readParameterWordsWireFormat(buffer, bufferIndex);