Guillaume Nodet <******@****.***> 1729859506 +0200

## Tools {#tools}

### Go command {#go-command}

## Tools {#tools}

### Go command {#go-command}

### Cgo {#cgo}

Cgo currently refuses to compile calls to a C function which has multiple
incompatible declarations. For instance, if `f` is declared as both `void f(int)`
and `void f(double)`, cgo will report an error instead of possibly generating an
incorrect call sequence for `f(0)`. New in this release is a better detector for
this error condition when the incompatible declarations appear in different

ENV BAZEL_LLVM "C:\tools\LLVM"

# These variables may be useful, but so far haven't been. Keeping for posterity.
# ENV CLANG_COMPILER_PATH "C:\tools\llvm\bin\clang.exe"
# ENV CC "C:\tools\llvm\bin\clang.exe"
# ENV BAZEL_COMPILER "C:\tools\llvm\bin\clang.exe"

ENV BAZEL_SH "C:\tools\msys64\usr\bin\bash.exe"

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.api;

import javax.tools.DocumentationTool;
import javax.tools.JavaFileManager;
import javax.tools.StandardLocation;

import java.io.File;
import java.nio.file.Path;
import java.util.Objects;
import java.util.Optional;
import java.util.StringJoiner;

  do_external_licenses_check \
    "//tensorflow/tools/pip_package:wheel" \
    "//tensorflow/tools/pip_package:licenses"
}

@test "Libtensorflow generated license includes all dependencies' licenses" {
  do_external_licenses_check \
    "//tensorflow:libtensorflow.so" \
    "//tensorflow/tools/lib_package:clicenses_generate"
}

test:linux_libtensorflow_test --config=cuda_wheel -- //tensorflow/tools/lib_package:libtensorflow_test //tensorflow/tools/lib_package:libtensorflow_java_test
build:linux_libtensorflow_build --config=cuda_wheel -- //tensorflow/tools/lib_package:libtensorflow.tar.gz //tensorflow/tools/lib_package:libtensorflow_jni.tar.gz //tensorflow/java:libtensorflow.jar //tensorflow/java:libtensorflow-src.jar //tensorflow/tools/lib_package:libtensorflow_proto.zip

/xla_configure.bazelrc
/bazel-*
/bazel_pip
/tools/python_bin_path.sh
/tensorflow/tools/git/gen
/pip_test
/_python_build
*.pyc
__pycache__
*.swp
.vscode/
cmake_build/
tensorflow/contrib/cmake/_build/
.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp
/tensorflow/lite/gen/**
/tensorflow/lite/tools/make/downloads/**

  do_external_licenses_check \
    "//tensorflow/tools/pip_package:build_pip_package" \
    "//tensorflow/tools/pip_package:licenses"
}

@test "Libtensorflow generated license includes all dependencies' licenses" {
  do_external_licenses_check \
    "//tensorflow:libtensorflow.so" \
    "//tensorflow/tools/lib_package:clicenses_generate"
}

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* **Uvicorn**: an ASGI server