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.github/ISSUE_TEMPLATE/tflite-other.md
- type: dropdown id: source attributes: label: Source description: Tensorflow
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docs/bucket/notifications/README.md
The python program below waits on mqtt topic `/minio` and prints event notifications on the console. We use [paho-mqtt](https://pypi.python.org/pypi/paho-mqtt/) library to do this. ```py #!/usr/bin/env python3 from __future__ import print_function import paho.mqtt.client as mqtt # This is the Subscriber def on_connect(client, userdata, flags, rc): print("Connected with result code "+str(rc)) # qos level is set to 1
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maven-core/src/site/apt/offline-mode.apt
** Control over downloads Find the control point for m2 using maven-wagon. At this point, inject a offline status parameter which is used when retrieving the specific Wagon. If <<<offline == true>>>: * If the wagon is not bound to "file://", then ignore the request and print a debug message. * If the wagon is bound to "file://" then:
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okhttp-dnsoverhttps/src/test/java/okhttp3/dnsoverhttps/TestDohMain.kt
private fun runBatch( dnsProviders: List<DnsOverHttps>, names: List<String>, ) { var time = System.currentTimeMillis() for (dns in dnsProviders) { println("Testing ${dns.url}") for (host in names) { print("$host: ") System.out.flush() try { val results = dns.lookup(host) println(results) } catch (uhe: UnknownHostException) { var e: Throwable? = uhe while (e != null) {
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Makefile
# environment. This is needed to allow overrides from Makefile.overrides.mk. export RUN = ./common/scripts/run.sh MAKE_DOCKER = $(RUN) make --no-print-directory -e -f Makefile.core.mk %: @$(MAKE_DOCKER) $@ default: @$(MAKE_DOCKER) shell: @$(RUN) /bin/bash .PHONY: default shell else
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ci/official/containers/linux_arm64/devel.bashrc
# See the License for the specific language governing permissions and # limitations under the License. # # ============================================================================== # Do not print anything if this is not being used interactively [ -z "$PS1" ] && return # Set up attractive prompt export PS1="\[\e[31m\]tf-docker\[\e[m\] \[\e[33m\]\w\[\e[m\] > " export TERM=xterm-256color
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.teamcity/src/main/kotlin/configurations/Gradleception.kt
workingDir = "%teamcity.build.checkoutDir%/dogfood-first-for-hash" scriptContent = """ set -x MD5=`find . -type f | sort | xargs md5sum | md5sum | awk '{ print $1 }'` echo "##teamcity[setParameter name='env.ORG_GRADLE_PROJECT_versionQualifier' value='gradleception-${'$'}MD5']" """.trimIndent() } gradleWrapper {
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analysis/analysis-api-fe10/src/org/jetbrains/kotlin/analysis/api/descriptors/utils/KtFe10DebugTypeRenderer.kt
append("@") if (classId != null) { append("R|") renderFqName(classId.asSingleFqName(), printer) append("|") } else { print("<ERROR TYPE REF>") } printCollection(namedValues, separator = ", ", prefix = "(", postfix = ")") { argument -> append(argument.name.render()) append(" = ")
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docs/distributed/DESIGN.md
atTotal += pool.Available if atTotal > choose && pool.Available > 0 { return pool.Index } } // Should not happen, but print values just in case. panic(fmt.Errorf("reached end of serverPools (total: %v, atTotal: %v, choose: %v)", total, atTotal, choose)) } ``` ## Other usages ### Advanced use cases with multiple ellipses
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docs/pt/docs/tutorial/extra-models.md
e depois chamarmos: ```Python user_dict = user_in.dict() ``` agora temos um `dict` com os dados na variável `user_dict` (é um `dict` em vez de um objeto de modelo Pydantic). E se chamarmos: ```Python print(user_dict) ``` teríamos um `dict` Python com: ```Python { 'username': 'john', 'password': 'secret', 'email': '******@****.***', 'full_name': None, } ```
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