override fun close() {
      if (closed) return
      closed = true
      detachTimeout(timeout)
      state = STATE_READ_RESPONSE_HEADERS
    }
  }

  /**
   * An HTTP body with alternating chunk sizes and chunk bodies. It is the caller's responsibility
   * to buffer chunks; typically by using a buffered sink with this sink.
   */
  private inner class ChunkedSink : Sink {

      // side_input_scale and not relevant in this case.
      auto sizes = mlir::DenseI32ArrayAttr::get(context, {1, 1, 1, 0});
      auto attr_name =
          StringAttr::get(context, mlir::OpTrait::AttrSizedOperandSegments<
                                       void>::getOperandSegmentSizeAttr());
      attrs.push_back(NamedAttribute(attr_name, sizes));
    }

	// length in octets of the RSA modulus n." 🙄
	//
	// This is extremely annoying, as all other encrypt and decrypt inputs are
	// always the exact same size as the modulus. Since it only happens for
	// weird modulus sizes, fix it by padding inefficiently.
	if emLen, k := len(em), priv.Size(); emLen < k {
		emNew := make([]byte, k)
		copy(emNew[k-emLen:], em)
		em = emNew
	}

	return decrypt(priv, em, withCheck)
}

// utilization distribution for that window size.
//
// Render plot progressively so rough outline is visible quickly even
// for very complex MUTs. Start by computing just a few window sizes
// and then add more window sizes.
//
// Consider using sampling to compute an approximate MUT. This would
// work by sampling the mutator utilization at randomly selected
// points in time in the trace to build an empirical distribution. We

			for i := 0; i < b.N; i++ {
				n.setBytes(buf[:l])
			}
		})
	}
}

func TestNatDiv(t *testing.T) {
	sizes := []int{
		1, 2, 5, 8, 15, 25, 40, 65, 100,
		200, 500, 800, 1500, 2500, 4000, 6500, 10000,
	}
	for _, i := range sizes {
		for _, j := range sizes {
			a := rndNat1(i)
			b := rndNat1(j)
			// the test requires b >= 2
			if len(b) == 1 && b[0] == 1 {
				b[0] = 2
			}

				// report parse errors.
				err = nil
			}
			return nil, err
		}
		files = append(files, f)
	}
	tc := &types.Config{
		Importer:  makeTypesImporter(cfg, fset),
		Sizes:     types.SizesFor("gc", build.Default.GOARCH), // TODO(adonovan): use cfg.Compiler
		GoVersion: cfg.GoVersion,
	}
	info := &types.Info{
		Types:      make(map[ast.Expr]types.TypeAndValue),

	exactSize := func(size int) checker.SizeEstimate {
		return checker.SizeEstimate{Min: uint64(size), Max: uint64(size)}
	}
	exactSizes := func(sizes ...int) []checker.SizeEstimate {
		results := make([]checker.SizeEstimate, len(sizes))
		for i, size := range sizes {
			results[i] = exactSize(size)
		}
		return results
	}
	cases := []struct {
		name       string
		function   string

	VCIPHER		V0, V1, V1
	VCIPHERLAST	V1, V2, V2

	// Store the result in BE order.
	P8_STXVB16X(V2, R3, R0)
	RET

Linvalid_key_len:
	// Segfault, this should never happen. Only 3 keys sizes are created/used.
	MOVD	R0, 0(R0)
	RET

// func decryptBlockAsm(nr int, xk *uint32, dst, src *byte)
TEXT ·decryptBlockAsm(SB), NOSPLIT|NOFRAME, $0
	MOVD	nr+0(FP), R6   // Round count/Key size

	return newGCMWithNonceAndTagSize(cipher, size, gcmTagSize)
}

// NewGCMWithTagSize returns the given 128-bit, block cipher wrapped in Galois
// Counter Mode, which generates tags with the given length.
//
// Tag sizes between 12 and 16 bytes are allowed.
//
// Only use this function if you require compatibility with an existing
// cryptosystem that uses non-standard tag lengths. All other users should use

// Converts tensor list operations into operations on buffers and sizes. Needs
// static shapes and known max element count.
std::unique_ptr<OperationPass<ModuleOp>> CreateTensorListOpsDecompositionPass();

// Converts tensor array ops into operations on local variables, which can later
// be removed by resource lifting. Requires known sizes and known element shapes
// (either defined in TensorArrayV3 or implied in the first write).