func validatePortNumber(path util.Path, val any) util.Errors {
	return validateIntRange(path, val, 0, 65535)
}

// validateIPRangesOrStar validates IP ranges and also allow star, examples: "1.1.0.256/16,2.2.0.257/16", "*"
func validateIPRangesOrStar(path util.Path, val any) (errs util.Errors) {
	scope.Debugf("validateIPRangesOrStar at %v: %v", path, val)

	if !util.IsString(val) {

                // Constraints also appear in the graph if they contributed to it.
                // Ignore them for now, though perhaps we can use them in the future to
                // publish version ranges.
                continue;
            }

            visitor.accept((ResolvedDependencyResult) dependencyResult);
        }
    }

    private static ModuleVersionIdentifier getVariantCoordinates(

      quant_specs->inference_input_type = DT_QINT8;
    }
  } else {
    // These flags are incompatible with post_training_quantize() as only
    // QAT models can provide required ranges.
    quant_specs->disable_infer_tensor_range =
        toco_flags.disable_infer_tensor_range();
    quant_specs->use_fake_quant_num_bits = toco_flags.use_fake_quant_num_bits();
  }

}

def MapFnOp : TensorflowMlrt_Op<"map_fn", [AttrSizedOperandSegments, Pure]> {
  let summary = "The Parallel Map for tf_mlrt dialect";
  let description = [{
    The Pmap executes body function in parallel for all ranges up to $max_iterations.

    The pseudo code:
      for(int i = 0; i < $max_iterations; i++) {
        body_fn(MlrtFture($tensor_list_or_flow_in[i]),
                MlrtPromise($tensor_list_or_flow_in[i+1]),

			addresses:    []string{"127.0.0.1/32", "10.20.30.40", "1.2.3.0/24"},
			expectedErrs: field.ErrorList{field.Invalid(newPath.Child("NodePortAddresses[1]"), "10.20.30.40", "must be a valid CIDR")},
		},
		"missing ipv6 subnet ranges": {
			addresses: []string{"::0", "::1", "2001:db8::/32"},
			expectedErrs: field.ErrorList{field.Invalid(newPath.Child("NodePortAddresses[0]"), "::0", "must be a valid CIDR"),

		// Old range is empty - use new one.
		return shadowRange(lo + hi<<16)
	}
	if hi < sr.lo() || lo > sr.hi() {
		// The two regions don't overlap or abut, so we would
		// have to keep track of multiple disjoint ranges.
		// Because we can only keep one, keep the larger one.
		if sr.hi()-sr.lo() >= hi-lo {
			return sr
		}
		return shadowRange(lo + hi<<16)
	}
	// Regions overlap or abut - compute the union.

	SSUBTYP_DWARNGE = 0x50000 // DWARF aranges section
	SSUBTYP_DWABREV = 0x60000 // DWARF abbreviation section
	SSUBTYP_DWSTR   = 0x70000 // DWARF strings section
	SSUBTYP_DWRNGES = 0x80000 // DWARF ranges section
	SSUBTYP_DWLOC   = 0x90000 // DWARF location lists section
	SSUBTYP_DWFRAME = 0xA0000 // DWARF frames section
	SSUBTYP_DWMAC   = 0xB0000 // DWARF macros section
)

// Symbol Table Entry.

    if (quantized_.contains(op)) continue;
    quantized_.insert(op);

    if (auto constant_op = dyn_cast<arith::ConstantOp>(op); constant_op) {
      // If the workflow requires inferring ranges from the content
      // (post-training quantization) and it is weight (filter) and hasn't
      // been quantized, we infer the quantization parameters from the content.

| HOST_PROBE_SNAT_IPV6 | "fd16:9254:7127:1337:ffff:ffff:ffff:ffff" | IPv6 link local ranges are designed to be collision-resistant by default, and so this probably never needs to be overridden |

## Sidecar Mode Implementation Details

In the example above, `A` would have to say it _strictly depends on 1.1_.

For this reason, a good practice is that if you use _strict versions_, you should express them in terms of ranges and a preferred version within this range.
For example, `B` might say, instead of `strictly 1.0`, that it _strictly depends_ on the `[1.0, 2.0[` range, but _prefers_ `1.0`.