// For architectures that support accelerated embedding lookups, this pass will
// rewrite the graph to use pipelining for better device utilization.
std::unique_ptr<OperationPass<ModuleOp>> CreateEmbeddingSequencingPass();

// This is a strictly sequential and formally correct fallback option for the
// embedding pipelining pass intended for debugging during pipelining
// development.
std::unique_ptr<OperationPass<ModuleOp>> CreateEmbeddingPipeliningPass();

include "mlir/Pass/PassBase.td"

def EmbeddingPipeliningPass : Pass<"tf-embedding-pipelining", "mlir::ModuleOp"> {
  let summary = "Rewrite graph for embedding pipelining";
  let constructor = "TFDevice::CreateEmbeddingPipeliningPass()";
    let description = [{
    For architectures that support accelerated embedding lookups, this pass will
    rewrite the graph to use pipelining for better device utilization.
  }];
}

          /*edge_filter=*/nullptr);
}

// This preserves the parallelism between pipeline stages.  For example, below
// is a typical pattern of input pipelining in Tensorflow and this heuristic
// ensures Node_X and Node_Y are put into different clusters.  Without the
// heuristic, they may be put into the same cluster and it can introduce

	}

	// TODO: only supports HTTP 1.1 for now.
	outReq.Proto = "HTTP/1.1"
	outReq.ProtoMajor = 1
	outReq.ProtoMinor = 1

	f.rewriter.Rewrite(outReq)

	// Disable closeNotify when method GET for http pipelining
	if outReq.Method == http.MethodGet {
		quietReq := outReq.WithContext(context.Background())
		*outReq = *quietReq
	}
}