Kaoru FUZITA <******@****.***> 1449825322 +0900

Kaoru FUZITA <******@****.***> 1449825322 +0900

 * the exact versions of tools they wish to use.</p>
 *
 * <p>
 * Toolchains can be obtained through the {@link org.apache.maven.api.services.ToolchainManager ToolchainManager}
 * service. This service provides methods to retrieve and manage toolchains defined
 * in the Maven configuration.
 * </p>
 *
 * <p>
 * The following are key functionalities provided by the Toolchain interface:</p><ul>

if [[ "${TFCI_MACOS_BAZEL_TEST_DIR_ENABLE}" == 1 ]]; then
  mkdir -p "${TFCI_MACOS_BAZEL_TEST_DIR_PATH}"
  export TEST_TMPDIR="${TFCI_MACOS_BAZEL_TEST_DIR_PATH}"
fi

# "TFCI_MACOS_INSTALL_BAZELISK_ENABLE" is used to decide if we need to install
# Bazelisk manually. We enable this for macOS x86 builds as those VMs do not
# have Bazelisk pre-installed. "TFCI_MACOS_INSTALL_BAZELISK_URL" contains the

So, you could add additional data to the automatically generated schema.

For example, you could decide to read and validate the request with your own code, without using the automatic features of FastAPI with Pydantic, but you could still want to define the request in the OpenAPI schema.

You could do that with `openapi_extra`:

## Singular values in body { #singular-values-in-body }

The same way there is a `Query` and `Path` to define extra data for query and path parameters, **FastAPI** provides an equivalent `Body`.

For example, extending the previous model, you could decide that you want to have another key `importance` in the same body, besides the `item` and `user`.

build:mkl --define=build_with_mkl=true --define=enable_mkl=true
build:mkl --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl --define=build_with_openmp=true
build:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

);\n\n  if (fragments[divider] && fragments[divider].indexOf(',') === -1) {\n    console.warn(\n      'Offsets separated by white space(s) are deprecated, use a comma (,) instead.'\n    );\n  }\n\n  // If divider is found, we divide the list of values and operands to divide\n  // them by ofset X and Y.\n  const splitRegex = /\\s*,\\s*|\\s+/;\n  let ops = divider !== -1\n    ? [\n        fragments\n          .slice(0, divider)\n          .concat([fragments[divider].split(splitRegex)[0]]),\n      ...

    subclasses. We've restored that support!


## Version 3.13.0

_2019-02-04_

 *  **This release bumps our minimum requirements to Java 8+ or Android 5+.** Cutting off old
    devices is a serious change and we don't do it lightly! [This post][require_android_5] explains
    why we're doing this and how to upgrade.

But it was created before there existed Python type hints. So, to define every <abbr title="the definition of how data should be formed">schema</abbr> you need to use specific utils and classes provided by Marshmallow.

/// check | Inspired **FastAPI** to

Use code to define "schemas" that provide data types and validation, automatically.

///