} else {
    bias_const_op = FindOperandOfType<stablehlo::ConstantOp>(op);
  }
  return isa<stablehlo::ConstantOp>(bias_const_op) ? bias_const_op : nullptr;
}

// Creates a new `tfl.qconst` op for the quantized filter. Transposes the
// filter value from [i, o] -> [o, i]. This is because we assume `[i, o]`
// format for `stablehlo.dot_general` (i.e. contracting dimension == 1)

		cases = append(cases, TrafficTestCase{
			name: d[0].Config().Service,
			// This creates a Gateway with a TCP listener that will accept TCP traffic from host
			// `fqdn` and forward that traffic back to `fqdn`, from srcPort to targetPort
			config: httpGateway("*", gatewayListenPort, gatewayListenPortName, "TCP", "") + // use the default label since this test creates its own gateway
				tcpVirtualService("gateway", fqdn, "", 80, ports.TCP.ServicePort),

    public static final String CONSTRAINTS_DecimalMax_MESSAGE = "{constraints.DecimalMax.message}";

    /** The key of the message: {item} must be greater than ${inclusive == true ? 'or equal to ' : ''}{value}. */
    public static final String CONSTRAINTS_DecimalMin_MESSAGE = "{constraints.DecimalMin.message}";

  }];
  let constructor = "TF::CreateHoistLoopInvariantPass()";
}

def LocalizeVarHandlesPass : Pass<"tf-localize-var-handles", "ModuleOp"> {
  let summary = "Creates VarHandleOps next to the operations that use them.";
  let description = [{
      Creates VarHandleOps right next to the operations that use them, one
      per operation.
      This is useful for transformations that only end up with a few small

					},
				},
			},
			plugins: buildScoreConfigDefaultWeights(scorePlugin1, scoreWithNormalizePlugin1),
			err:     true,
		},
		{
			name:    "Score plugin return score greater than MaxNodeScore",
			plugins: buildScoreConfigDefaultWeights(scorePlugin1),
			pluginConfigs: []config.PluginConfig{
				{
					Name: scorePlugin1,
					Args: &runtime.Unknown{

    return nullptr;
  }
  SmallVector<int64_t> new_shape = {lookup_type.getNumElements(),
                                    value_type.getDimSize(0)};
  return value_type.clone(new_shape);
}

// Creates FullyConnected op from params and returns the output.
mlir::Value GetFcOutput(OpBuilder *builder,
                        ::mlir::Operation::result_range result, Value input,
                        Value filter, Value bias,

	return kl.usernsManager.GetOrCreateUserNamespaceMappings(pod, runtimeHandler)
}

// GeneratePodHostNameAndDomain creates a hostname and domain name for a pod,
// given that pod's spec and annotations or returns an error.
func (kl *Kubelet) GeneratePodHostNameAndDomain(pod *v1.Pod) (string, string, error) {

  %size_splits = arith.constant dense<[-2]> : tensor<1xi32>
  %split_dim = arith.constant dense<0> : tensor<i32>
  // expected-error @+1 {{'tfl.split_v' op elements of 'size_splits' should be greater than or equal to -1}}
  %0 = "tfl.split_v"(%arg0, %size_splits, %split_dim) {num_splits = 1 : i32} : (tensor<16x4x4xf32>, tensor<1xi32>, tensor<i32>) -> tensor<16x4x4xf32>
  func.return %0 : tensor<16x4x4xf32>
}

        "properties": {
          "periodSeconds": {
            "default": 0,
            "description": "periodSeconds specifies the window of time for which the policy should hold true. PeriodSeconds must be greater than zero and less than or equal to 1800 (30 min).",
            "format": "int32",
            "type": "integer"
          },
          "type": {
            "default": "",

// this map.
using FeedsByNode = absl::flat_hash_map<
    absl::string_view,
    absl::flat_hash_map<int, const std::pair<std::string, ArrayInfo>*>>;

// Creates from a `GraphImportConfig::InputArrays` a mapping from a feeds output
// tensor name to index and ArrayInfo. Keys and values are backed by
// `GraphImportConfig::InputArrays`.
absl::StatusOr<FeedsByNode> GetFeedsByNode(