torch.min/max broadcasting about xla HOT 3 CLOSED

tensor([[[0.2436, 0.8010],
         [0.2436, 0.8010],
         [0.5483, 0.8010],
         [0.7193, 0.8010]],

        [[0.3733, 0.9327],
         [0.3733, 0.9327],
         [0.5483, 0.9327],
         [0.7193, 0.9327]],

        [[0.9229, 0.3699],
         [0.9229, 0.7067],
         [0.9229, 0.7648],
         [0.9229, 0.3699]]])
torch.Size([4, 2])

IR {
  %0 = f32[3,1,2]{2,1,0} xla::device_data(), device=CPU:0
  %1 = f32[4,2]{1,0} xla::device_data(), device=CPU:0
  %2 = f32[4,2]{1,0} aten::max(%1, %0)
}

HloModule DumpHloComputation.9

ENTRY %DumpHloComputation.9 (param_0.1: f32[3,1,2], param_1.2: f32[4,2]) -> (f32[3,4,2]) {
  %param_1.2 = f32[4,2]{1,0} parameter(1)
  %broadcast.3 = f32[3,4,2]{2,1,0} broadcast(f32[4,2]{1,0} %param_1.2), dimensions={1,2}
  %param_0.1 = f32[3,1,2]{2,1,0} parameter(0)
  %broadcast.4 = f32[3,1,2]{2,1,0} broadcast(f32[3,1,2]{2,1,0} %param_0.1), dimensions={0,1,2}
  %reshape.5 = f32[3,2]{1,0} reshape(f32[3,1,2]{2,1,0} %broadcast.4)
  %broadcast.6 = f32[3,4,2]{2,1,0} broadcast(f32[3,2]{1,0} %reshape.5), dimensions={0,2}
  %maximum.7 = f32[3,4,2]{2,1,0} maximum(f32[3,4,2]{2,1,0} %broadcast.3, f32[3,4,2]{2,1,0} %broadcast.6)
  ROOT %tuple.8 = (f32[3,4,2]{2,1,0}) tuple(f32[3,4,2]{2,1,0} %maximum.7)
}