interesaaat commented on issue #6300:
URL: https://github.com/apache/incubator-tvm/issues/6300#issuecomment-676775661
This is the full log in case:
```
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
WARNING:root:Untyped Tensor found, assume it is float32
fn (%v_operator_map.SklearnLGBMRegressor.values: Tensor[(15, 1), float32],
%input: Tensor[(3, 2), float32], %v_operator_map.SklearnLGBMRegressor.features:
Tensor[(15), int64], %v_operator_map.SklearnLGBMRegressor.nodes_offset:
Tensor[(1, 3), int64], %v_operator_map.SklearnLGBMRegressor.thresholds:
Tensor[(15), float32], %v_operator_map.SklearnLGBMRegressor.rights:
Tensor[(15), int64], %v_operator_map.SklearnLGBMRegressor.lefts: Tensor[(15),
int64]) -> Tensor[(3, 1), float32] {
%0 = cast(%v_operator_map.SklearnLGBMRegressor.nodes_offset,
dtype="int64") /* ty=Tensor[(1, 3), int64] */;
%1 = (%0, %0, %0);
%2 = concatenate(%1) /* ty=Tensor[(3, 3), int64] */;
%3 = reshape(%2, newshape=[-1]) /* ty=Tensor[(9), int64] */;
%4 = take(%v_operator_map.SklearnLGBMRegressor.features, %3, axis=0) /*
ty=Tensor[(9), int64] */;
%5 = reshape(%4, newshape=[-1, 3]) /* ty=Tensor[(3, 3), int64] */;
%6 = gather(%input, %5, axis=1) /* ty=Tensor[(3, 3), float32] */;
%7 = take(%v_operator_map.SklearnLGBMRegressor.thresholds, %3, axis=0) /*
ty=Tensor[(9), float32] */;
%8 = reshape(%7, newshape=[-1, 3]) /* ty=Tensor[(3, 3), float32] */;
%9 = greater_equal(%6, %8) /* ty=Tensor[(3, 3), bool] */;
%10 = take(%v_operator_map.SklearnLGBMRegressor.rights, %3, axis=0) /*
ty=Tensor[(9), int64] */;
%11 = reshape(%10, newshape=[-1, 3]) /* ty=Tensor[(3, 3), int64] */;
%12 = take(%v_operator_map.SklearnLGBMRegressor.lefts, %3, axis=0) /*
ty=Tensor[(9), int64] */;
%13 = reshape(%12, newshape=[-1, 3]) /* ty=Tensor[(3, 3), int64] */;
%14 = where(%9, %11, %13) /* ty=Tensor[(3, 3), int64] */;
%15 = cast(%14, dtype="int64") /* ty=Tensor[(3, 3), int64] */;
%16 = add(%15, %0) /* ty=Tensor[(3, 3), int64] */;
%17 = reshape(%16, newshape=[-1]) /* ty=Tensor[(9), int64] */;
%18 = take(%v_operator_map.SklearnLGBMRegressor.features, %17, axis=0) /*
ty=Tensor[(9), int64] */;
%19 = reshape(%18, newshape=[-1, 3]) /* ty=Tensor[(3, 3), int64] */;
%20 = gather(%input, %19, axis=1) /* ty=Tensor[(3, 3), float32] */;
%21 = take(%v_operator_map.SklearnLGBMRegressor.thresholds, %17, axis=0)
/* ty=Tensor[(9), float32] */;
%22 = reshape(%21, newshape=[-1, 3]) /* ty=Tensor[(3, 3), float32] */;
%23 = greater_equal(%20, %22) /* ty=Tensor[(3, 3), bool] */;
%24 = take(%v_operator_map.SklearnLGBMRegressor.rights, %17, axis=0) /*
ty=Tensor[(9), int64] */;
%25 = reshape(%24, newshape=[-1, 3]) /* ty=Tensor[(3, 3), int64] */;
%26 = take(%v_operator_map.SklearnLGBMRegressor.lefts, %17, axis=0) /*
ty=Tensor[(9), int64] */;
%27 = reshape(%26, newshape=[-1, 3]) /* ty=Tensor[(3, 3), int64] */;
%28 = where(%23, %25, %27) /* ty=Tensor[(3, 3), int64] */;
%29 = cast(%28, dtype="int64") /* ty=Tensor[(3, 3), int64] */;
%30 = add(%29, %0) /* ty=Tensor[(3, 3), int64] */;
%31 = reshape(%30, newshape=[-1]) /* ty=Tensor[(9), int64] */;
%32 = take(%v_operator_map.SklearnLGBMRegressor.values, %31, axis=0) /*
ty=Tensor[(9, 1), float32] */;
%33 = reshape(%32, newshape=[-1, 3, 1]) /* ty=Tensor[(3, 3, 1), float32]
*/;
%34 = reshape(%33, newshape=[-1, 1, 3]) /* ty=Tensor[(3, 1, 3), float32]
*/;
sum(%34, axis=[2]) /* ty=Tensor[(3, 1), float32] */
```
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