}
	return addr.Offset
}

// getRegister checks that addr represents a register and returns its value.
func (p *Parser) getRegister(prog *obj.Prog, op obj.As, addr *obj.Addr) int16 {
	if addr.Type != obj.TYPE_REG || addr.Offset != 0 || addr.Name != 0 || addr.Index != 0 {
		p.errorf("%s: expected register; found %s", op, obj.Dconv(prog, addr))
	}
	return addr.Reg
}

				a.Type = obj.TYPE_MEM
			}
			// fmt.Printf("CONST %d %s\n", a.Offset, obj.Dconv(&emptyProg, 0, a))
			p.expectOperandEnd()
			return
		}
		// fmt.Printf("offset %d \n", a.Offset)
	}

	// Register indirection: (reg) or (index*scale). We are on the opening paren.
	p.registerIndirect(a, prefix)
	// fmt.Printf("DONE %s\n", p.arch.Dconv(&emptyProg, 0, a))

	p.expectOperandEnd()
	return
}

		parser.start(lex.Tokenize(test.input))
		addr := obj.Addr{}
		parser.operand(&addr)
		var result string
		if parser.allowABI {
			result = obj.DconvWithABIDetail(&emptyProg, &addr)
		} else {
			result = obj.Dconv(&emptyProg, &addr)
		}
		if result != test.output {
			t.Errorf("fail at %s: got %s; expected %s\n", test.input, result, test.output)
		}
	}
}

func TestAMD64OperandParser(t *testing.T) {

	for i := ppc64.REG_R0; i <= ppc64.REG_R31; i++ {
		register[obj.Rconv(i)] = int16(i)
	}
	for i := ppc64.REG_F0; i <= ppc64.REG_F31; i++ {
		register[obj.Rconv(i)] = int16(i)
	}
	for i := ppc64.REG_V0; i <= ppc64.REG_V31; i++ {
		register[obj.Rconv(i)] = int16(i)
	}
	for i := ppc64.REG_VS0; i <= ppc64.REG_VS63; i++ {
		register[obj.Rconv(i)] = int16(i)
	}
	for i := ppc64.REG_A0; i <= ppc64.REG_A7; i++ {

set unit=%value:~-1%
rem assume the unit is specified
set conv=%value:~0,-1%

if "%unit%" == "k" goto kilo
if "%unit%" == "K" goto kilo
if "%unit%" == "m" goto mega
if "%unit%" == "M" goto mega
if "%unit%" == "g" goto giga
if "%unit%" == "G" goto giga

rem no unit found, must be bytes; consider the whole value
set conv=%value%
rem convert to KB
set /a conv=%conv% / 1024
:kilo
rem convert to MB

    follow-redirects "^1.0.0"
    requires-port "^1.0.0"

iconv-lite@0.4.24:
  version "0.4.24"
  resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.4.24.tgz#2022b4b25fbddc21d2f524974a474aafe733908b"
  integrity sha512-v3MXnZAcvnywkTUEZomIActle7RXXeedOR31wwl7VlyoXO4Qi9arvSenNQWne1TcRwhCL1HwLI21bEqdpj8/rA==
  dependencies:
    safer-buffer ">= 2.1.2 < 3"

iconv-lite@^0.6.3:
  version "0.6.3"

    *   `tf.data.experimental.service.register_dataset` now accepts optional
        `compression` argument.

*   Keras:

    *   `tf.keras.layers.Conv` now includes a public `convolution_op` method.
        This method can be used to simplify the implementation of Conv
        subclasses. There are two primary ways to use this new method. The first
        is to use the method directly in your own `call` method: `python class

Text","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallback","open...