if (scale && !IsOfRankedFloatTensorType(scale, 1))
    return op.emitOpError("requires scale to be a 1D float tensor");

  auto offset = GetRankedTensorTypeForOperand(op.getOffset());
  if (offset && !IsOfRankedFloatTensorType(offset, 1))
    return op.emitOpError("requires offset to be a 1D float tensor");

  auto mean = GetRankedTensorTypeForOperand(op.getMean());
  if (mean && !IsOfRankedFloatTensorType(mean, 1))

				switch st.kind {
				case abiStepIntReg:
					offset := add(s, st.offset, "precomputed value offset")
					intFromReg(&regArgs, st.ireg, st.size, offset)
				case abiStepPointer:
					s := add(s, st.offset, "precomputed value offset")
					*((*unsafe.Pointer)(s)) = regArgs.Ptrs[st.ireg]
				case abiStepFloatReg:
					offset := add(s, st.offset, "precomputed value offset")
					floatFromReg(&regArgs, st.freg, st.size, offset)

  let summary = "Return a slice from 'input'.";

  let description = [{
The output tensor is a tensor with dimensions described by 'size'
whose values are extracted from 'input' starting at the offsets in
'begin'.

`begin` is zero-based; `size` is one-based. If size[i] is -1, all remaining
elements in dimension i are included in the slice. In other words, this is
equivalent to setting: