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

# where fields are strings separated by whitespace.
export TF_VER_MAJOR=$(awk '/#define TF_MAJOR_VERSION/ {print $3}' tensorflow/core/public/version.h)
export TF_VER_MINOR=$(awk '/#define TF_MINOR_VERSION/ {print $3}' tensorflow/core/public/version.h)
export TF_VER_PATCH=$(awk '/#define TF_PATCH_VERSION/ {print $3}' tensorflow/core/public/version.h)

{{/* Default Prometheus is enabled if its enabled and there are no config overrides set */}} {{ define "default-prometheus" }} {{- and (not .Values.meshConfig.defaultProviders) .Values.telemetry.enabled .Values.telemetry.v2.enabled .Values.telemetry.v2.prometheus.enabled }} {{- end }} {{/* SD has metrics and logging split. Default metrics are enabled if SD is enabled */}} {{ define "default-sd-metrics" }} {{- and (not .Values.meshConfig.defaultProviders) .Values.telemetry.enabled .Values.telemetry.v2.enabled...

# Lifespan Events

You can define logic (code) that should be executed before the application **starts up**. This means that this code will be executed **once**, **before** the application **starts receiving requests**.

The same way, you can define logic (code) that should be executed when the application is **shutting down**. In this case, this code will be executed **once**, **after** having handled possibly **many requests**.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip
    When you want to define dependencies that should be compatible with both HTTP and WebSockets, you can define a parameter that takes an `HTTPConnection` instead of a `Request` or a `WebSocket`.

            if it should exclude from the output the fields that had the same default
            value, even when they were explicitly set.
            """
        ),
    ] = False,
    exclude_none: Annotated[
        bool,
        Doc(
            """
            Pydantic's `exclude_none` parameter, passed to Pydantic models to define

{{/* Default Prometheus is enabled if its enabled and there are no config overrides set */}} {{ define "default-prometheus" }} {{- and (not .Values.meshConfig.defaultProviders) .Values.telemetry.enabled .Values.telemetry.v2.enabled .Values.telemetry.v2.prometheus.enabled }} {{- end }} {{/* SD has metrics and logging split. Default metrics are enabled if SD is enabled */}} {{ define "default-sd-metrics" }} {{- and (not .Values.meshConfig.defaultProviders) .Values.telemetry.enabled .Values.telemetry.v2.enabled...

# `HTTPConnection` class

When you want to define dependencies that should be compatible with both HTTP and WebSockets, you can define a parameter that takes an `HTTPConnection` instead of a `Request` or a `WebSocket`.

You can import it from `fastapi.requests`:

```python
from fastapi.requests import HTTPConnection
```

    """
    API key authentication using a query parameter.

    This defines the name of the query parameter that should be provided in the request
    with the API key and integrates that into the OpenAPI documentation. It extracts
    the key value sent in the query parameter automatically and provides it as the
    dependency result. But it doesn't define how to send that API key to the client.

    ## Usage

config_setting(
    name = "dynamic_loaded_kernels",
    define_values = {
        "dynamic_loaded_kernels": "true",
        "framework_shared_object": "true",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "using_rocm_hipcc",
    define_values = {"using_rocm_hipcc": "true"},
)

config_setting(
    name = "override_eigen_strong_inline",
    define_values = {"override_eigen_strong_inline": "true"},