num_args: The number of inputs.
    device: The tensorflow device. eg. "/CPU:0"
    op_attrs: The tensorflow attributes excluding the func attrs.
    op_func_attrs: The func attrs.
    op_name: The tensorflow op name. eg. "tf.AddV2"

    Example:
      %out_ch = tfrt_fallback_async.createop(%in_ch) key(0) device("/CPU:0")

TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes
// `send_device`, `recv_device`, and `send_device_incarnation` set.
TFE_Op* SendOp(TFE_Context* ctx, TFE_TensorHandle* in,
               const std::string& op_name, const std::string& send_device,
               const std::string& recv_device,

	_ = x[OpMuxServerMsg-10]
	_ = x[OpUnblockSrvMux-11]
	_ = x[OpUnblockClMux-12]
	_ = x[OpAckMux-13]
	_ = x[OpRequest-14]
	_ = x[OpResponse-15]
	_ = x[OpDisconnect-16]
	_ = x[OpMerged-17]
}

const _Op_name = "ConnectConnectResponsePingPongConnectMuxMuxConnectErrorDisconnectClientMuxDisconnectServerMuxMuxClientMsgMuxServerMsgUnblockSrvMuxUnblockClMuxAckMuxRequestResponseDisconnectMerged"

  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,
                       absl::Span<AbstractTensorHandle* const> outputs,
                       GradientFunction* gradient_function,
                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has

    if (llvm::isa<TFL::CustomTfOp>(op) || llvm::isa<TFL::CustomOp>(op)) return;

    std::string op_name = op->getName().getIdentifier().str();
    // Wrap the operation, if
    // - the op is targeted explicitly, or
    // - the op isn't registered when there are no target list.
    if (target_op_names.contains(op_name) ||
        (target_op_names.empty() && !op->isRegistered())) {
      custom_ops.push_back(op);
    }
  });

#define TENSORFLOW_C_EAGER_TRACING_UTILS_H_

#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {
namespace tracing {
Status MaybeSetOpName(AbstractOperation*, const char* op_name);
}  // namespace tracing
}  // namespace tensorflow

    auto custom_op = llvm::dyn_cast_or_null<CustomOp>(op);
    if (!custom_op) return false;

    // Custom op which is marked in the custom op map is quantizable.
    std::string op_name = custom_op.getCustomCode().str();
    return (custom_op_map.find(op_name) == custom_op_map.end()) ? false : true;
  }

  static bool AllowDynamicRangeQuantizedOperand(
      Operation* quantized_op, const quant::CustomOpMap& custom_op_map) {

  OpBuilder builder(func);

  func.walk([&](Operation *op) {
    if (op->hasTrait<OpTrait::IsTerminator>()) return;
    auto op_name = op_to_name_(op);

    // Check the named info collection first.
    auto it = name_to_info_.find(op_name);
    if (it != name_to_info_.end()) {
      ImportAsStatsOps(builder, op, it->second.first, it->second.second);
      return;
    }

// The consolidated logic to verify if each final op is acceptable or not.
// Also see `PrintOpStatsPass` and `CheckAcceptedOpsPass`.
bool IsAcceptedOp(llvm::StringRef dialect_name, llvm::StringRef op_name,
                  const std::vector<std::string> &accepted_dialects);

}  // namespace odml
}  // namespace mlir

    public companion object {
        public val EP_NAME: ExtensionPointName<KaResolveExtensionProvider> =
            ExtensionPointName<KaResolveExtensionProvider>("org.jetbrains.kotlin.kaResolveExtensionProvider")

        public fun provideExtensionsFor(module: KtModule): List<KaResolveExtension> {
            return EP_NAME.getExtensionList(module.project).flatMap { it.provideExtensionsFor(module) }
        }
    }