Services int
	// Number of instances to make
	Instances int
	// If set, only run for this config type
	OnlyRunType string
	// If set, skip runs for this config type
	SkipType string
	// ResourceType of proxy to generate configs for. If not set, sidecar is used
	ProxyType model.NodeType
}

var testCases = []ConfigInput{
	{
		// Gateways provides an example config for a large Ingress deployment. This will create N

  for (auto it : llvm::enumerate(var_access_info.new_operand_values)) {
    Type type = it.value().getType();
    if (type.isa<TensorType>() &&
        type.cast<TensorType>().getElementType().isa<TF::ResourceType>()) {
      if (!llvm::is_contained(device_var_reads_indices, it.index()) &&
          !llvm::is_contained(device_var_updates_indices, it.index())) {
        return execute_launch.GetBody().front().emitError("operand #")

  return parsed_device.has_type && parsed_device.type == kTPUReplicatedCore;
}

bool TypeValidForXLA(const mlir::Type& type) {
  const mlir::Type elem = getElementTypeOrSelf(type);
  return !mlir::isa<mlir::TF::ResourceType>(elem) &&
         !mlir::isa<mlir::TF::StringType>(elem);
}

mlir::LogicalResult GetDeviceToHostMap(
    mlir::tf_device::ClusterOp cluster,
    llvm::SmallVector<std::string, 8>& core_to_host) {

      assert(while_caller.body_function() == func);
      while_callers.push_back(while_caller);
    }
  }
  return while_callers;
}

bool IsResourceType(Type type) {
  return mlir::isa<TF::ResourceType>(getElementTypeOrSelf(type));
}

bool OnlyOperatesOnCompositeDevices(
    TF::TPUExecuteAndUpdateVariablesOp& op,
    const TF::SideEffectAnalysis::Info& side_effect_analysis,
    const DataFlowSolver& solver) {

            auto etype, ConvertToMlirTensorType(shape_proto, dtype, &builder));
        return mlir::UnrankedTensorType::get(mlir::TF::ResourceType::get(
            {mlir::cast<TensorType>(etype)}, builder.getContext()));
      } else {
        return mlir::UnrankedTensorType::get(
            mlir::TF::ResourceType::get(builder.getContext()));
      }
    } else if (auto shape = node.attrs().Find("_output_shapes")) {

  for (auto val : input) types.push_back(val.getType());
  return types;
}

bool IsResourceType(Type val_type) {
  if (auto tensor_type = mlir::dyn_cast<mlir::TensorType>(val_type)) {
    if (mlir::isa<TF::ResourceType>(tensor_type.getElementType())) {
      return true;
    }
  }
  return false;
}

bool IsTPUOp(mlir::Operation* op) {
  return op->hasAttr(TF::kReplicationInfoAttr);
}

    auto elementType = getElementTypeOrSelf(operand.getType());
    if (mlir::isa<mlir::TF::ResourceType>(elementType)) {
      return true;
    }
  }
  for (const auto& result : op->getResults()) {
    auto elementType = getElementTypeOrSelf(result.getType());
    if (mlir::isa<mlir::TF::ResourceType>(elementType)) {
      return true;
    }
  }
  return false;
}

namespace mlir {
namespace TFDevice {
namespace {

bool IsResourceType(Type val_type) {
  if (auto tensor_type = mlir::dyn_cast<mlir::TensorType>(val_type)) {
    if (mlir::isa<TF::ResourceType>(tensor_type.getElementType())) {
      return true;
    }
  }
  return false;
}

struct EmbeddingPipeliningPass
    : public ::impl::EmbeddingPipeliningPassBase<EmbeddingPipeliningPass> {

    This pass promotes resource accesses in function(s) (by default, the main)
    to input arguments and outputs of the function(s).

    Two types of resources are supported:
    (1) A function argument of TF::ResourceType type (this pass).
    (2) A VarHandleOp in the function (tf-promote-var-handles-to-args).

    After the pass,

     . The function will have an input argument for each resource that is

This pass promotes resource accesses in function(s) (by default, the main)
to input arguments and outputs of the function(s).

Two types of resources are supported:
(1) A function argument of TF::ResourceType type (this pass).
(2) A VarHandleOp in the function (tf-promote-var-handles-to-args).

After the pass,

 . The function will have an input argument for each resource that is