*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.

	}
	return false
}

// IsRISCV64VTypeI reports whether op is a vtype immediate instruction that
// requires special handling.
func IsRISCV64VTypeI(op obj.As) bool {
	return op == riscv.AVSETVLI || op == riscv.AVSETIVLI
}

// IsRISCV64CSRO reports whether the op is an instruction that uses
// CSR symbolic names and whether that instruction expects a register
// or an immediate source operand.

    private final Range<C> restriction;

    SubRangeSet(Range<C> restriction) {
      super(
          new SubRangeSetRangesByLowerBound<C>(
              Range.all(), restriction, TreeRangeSet.this.rangesByLowerBound));
      this.restriction = restriction;
    }

    @Override
    public boolean encloses(Range<C> range) {
      if (!restriction.isEmpty() && restriction.encloses(range)) {

Instead, the compiler operates on a kind of semi-abstract instruction set,
and instruction selection occurs partly after code generation.
The assembler works on the semi-abstract form, so
when you see an instruction like <code>MOV</code>
what the toolchain actually generates for that operation might
not be a move instruction at all, perhaps a clear or load.
Or it might correspond exactly to the machine instruction with that name.

        }

        VersionRange range = artifact.getVersionRange();
        if (range != null) {
            for (Restriction restriction : range.getRestrictions()) {
                if (isSnapshot(restriction.getLowerBound()) || isSnapshot(restriction.getUpperBound())) {
                    return RELEASE_OR_SNAPSHOT;
                }
            }
        }

        return RELEASE;
    }

		{"VADDPD.RZ_SAE.SAE X0, X1, X2", `bad suffix combination`},

		// BSWAP on 16-bit registers is undefined. See #29167,
		{"BSWAPW DX", `unrecognized instruction`},
		{"BSWAPW R11", `unrecognized instruction`},
	})
}

func testBadInstParser(t *testing.T, goarch string, tests []badInstTest) {
	for i, test := range tests {
		arch, ctxt := setArch(goarch)

                                                             TF_Status* status,
                                                             TFE_Context* ctx);

// Calls the destructor of DLManagedTensor, used in the destructor of PyCapsule.
TF_CAPI_EXPORT extern void TFE_CallDLManagedTensorDeleter(void* dlm_ptr);
}  // namespace tensorflow

    /** Logon failure: unknown user name or bad password */
    int NT_STATUS_LOGON_FAILURE = 0xC000006d;
    /** Logon failure: user account restriction */
    int NT_STATUS_ACCOUNT_RESTRICTION = 0xC000006e;
    /** Logon failure: account logon time restriction violation */
    int NT_STATUS_INVALID_LOGON_HOURS = 0xC000006f;
    /** Logon failure: user not allowed to log on to this computer */

		}
		p.errorf("wrong number of arguments to %s instruction", op)
		return
	case 4:
		if p.arch.Family == sys.S390X || p.arch.Family == sys.PPC64 {
			// 4-operand compare-and-branch.
			prog.From = a[0]
			prog.Reg = p.getRegister(prog, op, &a[1])
			prog.AddRestSource(a[2])
			target = &a[3]
			break
		}
		p.errorf("wrong number of arguments to %s instruction", op)
		return
	default:

# Tester les WebSockets { #testing-websockets }

Vous pouvez utiliser le même `TestClient` pour tester les WebSockets.

Pour cela, vous utilisez `TestClient` dans une instruction `with`, en vous connectant au WebSocket :

{* ../../docs_src/app_testing/tutorial002_py310.py hl[27:31] *}

/// note | Remarque