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++ {

	}
	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
}

			conv.getTypeIDs[n.Go[:len(n.Go)-9]] = true
		}
	}
	for i, n := range names {
		if types[i] == nil {
			continue
		}
		pos := f.NamePos[n]
		f, fok := types[i].(*dwarf.FuncType)
		if n.Kind != "type" && fok {
			n.Kind = "func"
			n.FuncType = conv.FuncType(f, pos)
		} else {
			n.Type = conv.Type(types[i], pos)
			switch n.Kind {
			case "iconst":

    *   `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

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