// 31.13.14: Vector Floating-Point Classify Instruction
	VFCLASSV	V2, V3				// d711284e
	VFCLASSV	V2, V0, V3			// d711284c

	// 31.13.15: Vector Floating-Point Merge Instruction
	VFMERGEVFM	F10, V2, V0, V3			// d751255c

	// 31.13.16: Vector Floating-Point Move Instruction
	VFMVVF		F10, V3				// d751055e

	// 31.13.17: Single-Width Floating-Point/Integer Type-Convert Instructions
	VFCVTXUFV	V2, V3				// d711204a

	MOVV		(R14)(R13), R12	// cc350c38

	// STX.{B,H,W,D} instructions
	MOVB		R12, (R14)(R13)	// cc351038
	MOVH		R12, (R14)(R13)	// cc351438
	MOVW		R12, (R14)(R13)	// cc351838
	MOVV		R12, (R14)(R13)	// cc351c38

	// FLDX.{S,D} instructions
	MOVF		(R14)(R13), F2	// c2353038
	MOVD		(R14)(R13), F2	// c2353438

	// FSTX.{S,D} instructions
	MOVF		F2, (R14)(R13)	// c2353838
	MOVD		F2, (R14)(R13)	// c2353c38

			}
		} else if p.arch.Family == sys.Loong64 {
			if arch.IsLoong64RDTIME(op) {
				// The Loong64 RDTIME family of instructions is a bit special,
				// in that both its register operands are outputs
				prog.To = a[0]
				if a[1].Type != obj.TYPE_REG {
					p.errorf("invalid addressing modes for 2nd operand to %s instruction, must be register", op)
					return
				}
				prog.RegTo2 = a[1].Reg
				break
			}

            <phase>process-classes</phase>
            <goals>
              <goal>manifest</goal>
            </goals>
          </execution>
        </executions>
        <configuration>
          <instructions>
            <!-- Silence a warning that claims that META-INF/versions/9/... is the "wrong directory" for our classes. -->
            <_fixupmessages>^Classes found in the wrong directory: .*</_fixupmessages>

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

labels: [lang-all]
body:
  - type: markdown
    attributes:
      value: |
        Thanks for your interest in helping translate the FastAPI docs! 🌍

        Please follow these instructions carefully to propose a new language translation. 🙏

        This structured process helps ensure translations can be properly maintained long-term.
  - type: checkboxes
    id: checks
    attributes:
      label: Initial Checks

			</plugin>
			<plugin>
				<groupId>org.apache.felix</groupId>
				<artifactId>maven-bundle-plugin</artifactId>
				<version>6.0.0</version>
				<extensions>true</extensions>

				<configuration>
					<instructions>
						<Bundle-Version>${osgi.version}</Bundle-Version>
						<Bundle-License>LGPL</Bundle-License>
						<Bundle-Vendor>CodeLibs</Bundle-Vendor>
						<Bundle-SymbolicName>org.codelibs.jcifs</Bundle-SymbolicName>

- 🔑 JWT token authentication.
- 📫 Email based password recovery.
- ✅ Tests with [Pytest](https://pytest.org).
- 📞 [Traefik](https://traefik.io) as a reverse proxy / load balancer.
- 🚢 Deployment instructions using Docker Compose, including how to set up a frontend Traefik proxy to handle automatic HTTPS certificates.

4. [Install Certificates from Third-party CAs](#install-certificates-from-third-party-cas)

## 1. Install MinIO Server

Install MinIO Server using the instructions in the [MinIO Quickstart Guide](https://docs.min.io/community/minio-object-store/operations/deployments/baremetal-deploy-minio-on-redhat-linux.html).

## 2. Use an Existing Key and Certificate with MinIO

import okhttp3.internal.platform.Platform

/**
 * Example of using a hardware key to perform client auth.
 * Prefer recent JDK builds, and results are temperamental to slight environment changes.
 * Different instructions and configuration may be required for other hardware devices.
 *
 * Using a yubikey device as a SSL key store.
 * https://lauri.võsandi.com/2017/03/yubikey-for-ssh-auth.html
 *
 * Using PKCS11 support in the JDK.