isErr:         false,
			expectedCurve: elliptic.P256(),
		},
		"ECDSA-P384": {
			key:           ecdsaPrivKeyP384,
			isErr:         false,
			expectedCurve: elliptic.P384(),
		},
		"ECDSA-P512": {
			key:           ecdsaPrivKeyP521,
			isErr:         false,
			expectedCurve: elliptic.P256(),
		},
		"ED25519": {
			key:           ed25519PrivKey,
			isErr:         true,
			expectedCurve: nil,

	return 0x6B<<25 | 0<<23 | x<<21 | 0x1F<<16 | 0<<10
}

func SYSOP(l uint32, op0 uint32, op1 uint32, crn uint32, crm uint32, op2 uint32, rt uint32) uint32 {
	return 0x354<<22 | l<<21 | op0<<19 | op1<<16 | crn&15<<12 | crm&15<<8 | op2<<5 | rt
}

func SYSHINT(x uint32) uint32 {
	return SYSOP(0, 0, 3, 2, 0, x, 0x1F)
}

func LDSTR(sz uint32, v uint32, opc uint32) uint32 {
	return sz<<30 | 7<<27 | v<<26 | opc<<22
}

	//      ios/arm64         33         4MB           1  2048  (8KB)
	//       */32-bit         32         4MB           1  1024  (4KB)
	//     */mips(le)         31         4MB           1   512  (2KB)

	// heapArenaBytes is the size of a heap arena. The heap
	// consists of mappings of size heapArenaBytes, aligned to
	// heapArenaBytes. The initial heap mapping is one arena.
	//

* 🌐 Update Chinese translation for `/docs/advanced/security/http-basic-auth.md`. PR [#11512](https://github.com/tiangolo/fastapi/pull/11512) by [@nick-cjyx9](https://github.com/nick-cjyx9).

### Internal

* 👥 Update FastAPI People. PR [#11669](https://github.com/tiangolo/fastapi/pull/11669) by [@tiangolo](https://github.com/tiangolo).

def LstmCifgInputConstraint : PredOpTrait<
  "the cifg inputs should all be specified or none",
   // If LSTM has combined input/forget gate, input 1, 5, 9, 12, 20 are all none
   // or 1, 5, 12 should not be none. Inputs 9 and 20 depend on LSTM's variants.
   Or<[
     And<[TypeIsPred<"input_to_input_weights", NoneType>,
          TypeIsPred<"recurrent_to_input_weights", NoneType>,

  )
  @test_util.run_in_graph_and_eager_modes
  def test_matmul_model(
      self,
      target_opset: quant_opts_pb2.OpSet,
      enable_per_channel_quantization: bool,
  ):
    input_shape = (1, 512)

    self._create_matmul_model(
        input_shape=input_shape,
        weight_shape=(512, 2),
        saved_model_path=self._input_saved_model_path,
    )

    tags = {tag_constants.SERVING}