Kapica, Peng Yu, Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu
Wang, Rajendra Arora, Rasmus, Renat Idrisov, resec, Robin Richtsfeld, Ronald
Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri, Santiago Castro, Sayed
Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash
Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental,
starsblinking, Stephen Lumenta, Steven Hickson, Su Tang, Taehoon Lee, Takuya

numpy ~= 1.23.5 ; python_version <= "3.11"
numpy ~= 1.26.0 ; python_version >= "3.12"
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

Quick Build
-------
If you want to bootstrap Maven, you'll need:
- Java 17+
- Maven 3.6.3 or later
- Run Maven, specifying a location into which the completed Maven distro should be installed:
    ```
    mvn -DdistributionTargetDir="$HOME/app/maven/apache-maven-4.0.x-SNAPSHOT" clean package
    ```


[home]: https://maven.apache.org/
[jira]: https://issues.apache.org/jira/projects/MNG/

      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-assembly-plugin</artifactId>
        <executions>
          <execution>
            <id>create-distro-packages</id>
            <goals>
              <goal>single</goal>
            </goals>
            <phase>package</phase>
            <configuration>
              <descriptors>

		g.Expect(objs.kind(name.RoleStr).nameMatches(".*gateway.*").size()).Should(Equal(3))
		g.Expect(objs.kind(name.RoleBindingStr).nameMatches(".*gateway.*").size()).Should(Equal(3))
		g.Expect(objs.kind(name.SAStr).nameMatches(".*gateway.*").size()).Should(Equal(3))

		dobj := mustGetDeployment(g, objs, "istio-ingressgateway")
		d := dobj.Unstructured()
		c := dobj.Container("istio-proxy")

absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e
    # via -r requirements.in
astunparse==1.6.3 \

absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e
    # via -r requirements.in
astunparse==1.6.3 \

absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e
    # via -r requirements.in
astunparse==1.6.3 \

absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e
    # via -r requirements.in
astunparse==1.6.3 \

Você pega seus hambúrgueres e vai para a mesa com seu _crush_ :heart_eyes:.

Vocês comem os hambúrgueres, e o trabalho está terminado.

Não houve muita conversa ou flerte já que a maior parte do tempo foi gasto esperando os lanches na frente do balcão.

---