// it's shared to avoid using too much memory in tests
    long[] sharedArray = new long[arraysDim2];
    Arrays.fill(arrays, sharedArray);

    try {
      Longs.concat(arrays);
      fail();
    } catch (IllegalArgumentException expected) {
    }
  }

  private static void assertByteArrayEquals(byte[] expected, byte[] actual) {
    assertWithMessage(

    } else {
      Cut<C> newLower = (lowerCmp >= 0) ? lowerBound : connectedRange.lowerBound;
      Cut<C> newUpper = (upperCmp <= 0) ? upperBound : connectedRange.upperBound;

      // create() would catch this, but give a confusing error message
      checkArgument(
          newLower.compareTo(newUpper) <= 0,
          "intersection is undefined for disconnected ranges %s and %s",
          this,

 *  **Fix: Disable ALPN on Android 4.4.** That release of the feature was
    unstable and prone to native crashes in the underlying OpenSSL code.
 *  Fix: Don't send both `If-None-Match` and `If-Modified-Since` cache headers
    when both are applicable.
 *  Fix: Fail early when a port is out of range.
 *  Fix: Offer `Content-Length` headers for multipart request bodies.

      // TODO(lowasser): could be potentially optimized, but only with
      // extensive testing
      try {
        return Float.parseFloat(string);
      } catch (NumberFormatException e) {
        // Float.parseFloat has changed specs several times, so fall through
        // gracefully
      }
    }
    return null;
  }

    if (FLOATING_POINT_PATTERN.matcher(string).matches()) {
      // TODO(lowasser): could be potentially optimized, but only with
      // extensive testing
      try {
        return Double.parseDouble(string);
      } catch (NumberFormatException e) {
        // Double.parseDouble has changed specs several times, so fall through
        // gracefully
      }
    }
    return null;
  }

      // TODO(lowasser): could be potentially optimized, but only with
      // extensive testing
      try {
        return Float.parseFloat(string);
      } catch (NumberFormatException e) {
        // Float.parseFloat has changed specs several times, so fall through
        // gracefully
      }
    }
    return null;
  }

        } else {
          value = tokenizer.consumeToken(TOKEN_MATCHER);
        }
        parameters.put(attribute, value);
      }
      return create(type, subtype, parameters.build());
    } catch (IllegalStateException e) {
      throw new IllegalArgumentException("Could not parse '" + input + "'", e);
    }
  }

  private static void consumeSeparator(Tokenizer tokenizer, char c) {

			return err
		}
		err = werr
	}
}

// stringsHasPrefixFold tests whether the string s begins with prefix ignoring case.
func stringsHasPrefixFold(s, prefix string) bool {
	// Test match with case first.
	return len(s) >= len(prefix) && (s[0:len(prefix)] == prefix || strings.EqualFold(s[0:len(prefix)], prefix))
}

func ptr[T any](a T) *T {
	return &a
}

        """
        Add a *path operation* using an HTTP PATCH operation.

        ## Example

        ```python
        from fastapi import FastAPI
        from pydantic import BaseModel

        class Item(BaseModel):
            name: str
            description: str | None = None

        app = FastAPI()

        @app.patch("/items/")
        def update_item(item: Item):

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.