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input.mlir

module {
  dataflow.graph private @graph_0(
      %start: none, %i: index, %c: i1, %m: vector<4xi1>,
      %av: vector<4xindex>, %val: i32,
      %a: memref<16xi32>, %b: memref<16xi32>) -> ()
      attributes {input_segments = array<i32: 5, 0, 2>,
                  result_segments = array<i32: 0, 0, 0>} {
    %pad = arith.constant 0 : i32
    %g0, %gd0 = dataflow.load %a[%av] %start mask %m : memref<16xi32>, vector<4xindex>, vector<4xi32>
    %sc0 = dataflow.store %b[%av] %g0 %start mask %m : memref<16xi32>, vector<4xindex>, vector<4xi32>
    %lb1 = arith.constant 0 : index
    %ub1 = arith.constant 4 : index
    %sp1 = arith.constant 1 : index
    scf.for %k1 = %lb1 to %ub1 step %sp1 {
    %v2 = vector.transfer_read %b[%i], %pad, %m {in_bounds = [true]} : memref<16xi32>, vector<4xi32>
    vector.transfer_write %v2, %a[%i], %m {in_bounds = [true]} : vector<4xi32>, memref<16xi32>
    }
    %g3, %gd3 = dataflow.load %a[%av] %start mask %m : memref<16xi32>, vector<4xindex>, vector<4xi32>
    %sc3 = dataflow.store %b[%av] %g3 %start mask %m : memref<16xi32>, vector<4xindex>, vector<4xi32>
    dataflow.graph.return %start : none
  }
}