program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.2.1"}, {"coremlc-version", "3520.2.1"}, {"mldb_token", "mldb-rxg5jd7ao8"}})]
{
    func main<ios15>(tensor<fp32, [1, 13]> input_scores) {
            tensor<int32, [2]> name_scores_begin_0 = const()[name = tensor<string, []>("name_scores_begin_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> name_scores_end_0 = const()[name = tensor<string, []>("name_scores_end_0"), val = tensor<int32, [2]>([1, 7])];
            tensor<bool, [2]> name_scores_end_mask_0 = const()[name = tensor<string, []>("name_scores_end_mask_0"), val = tensor<bool, [2]>([true, false])];
            tensor<string, []> input_scores_to_fp16_dtype_0 = const()[name = tensor<string, []>("input_scores_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 13]> cast_8 = cast(dtype = input_scores_to_fp16_dtype_0, x = input_scores);
            tensor<fp16, [1, 7]> name_scores_cast = slice_by_index(begin = name_scores_begin_0, end = name_scores_end_0, end_mask = name_scores_end_mask_0, x = cast_8);
            tensor<fp16, [7]> name_weights_to_fp16 = const()[name = tensor<string, []>("name_weights_to_fp16"), val = tensor<fp16, [7]>([0x1p+0, 0x1.cccp-1, 0x1.998p-1, 0x1.998p-1, 0x1.668p-1, 0x1.cccp-1, 0x1.334p-1])];
            tensor<fp16, [1, 7]> var_15_cast = mul(x = name_scores_cast, y = name_weights_to_fp16);
            tensor<int32, [1]> reduce_max_0_axes_0 = const()[name = tensor<string, []>("reduce_max_0_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> reduce_max_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_max_0_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1]> reduce_max_0_cast = reduce_max(axes = reduce_max_0_axes_0, keep_dims = reduce_max_0_keep_dims_0, x = var_15_cast);
            tensor<string, []> reduce_max_0_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("reduce_max_0_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, [2]> input_1_begin_0 = const()[name = tensor<string, []>("input_1_begin_0"), val = tensor<int32, [2]>([0, 7])];
            tensor<int32, [2]> input_1_end_0 = const()[name = tensor<string, []>("input_1_end_0"), val = tensor<int32, [2]>([1, 13])];
            tensor<bool, [2]> input_1_end_mask_0 = const()[name = tensor<string, []>("input_1_end_mask_0"), val = tensor<bool, [2]>([true, true])];
            tensor<fp16, [1, 6]> input_1_cast = slice_by_index(begin = input_1_begin_0, end = input_1_end_0, end_mask = input_1_end_mask_0, x = cast_8);
            tensor<fp16, [1, 6]> neighborhood_agg_weight_to_fp16 = const()[name = tensor<string, []>("neighborhood_agg_weight_to_fp16"), val = tensor<fp16, [1, 6]>([[0x1.8p+1, 0x1.998p-3, 0x1p+1, 0x1p+1, 0x1p+0, 0x1p+0]])];
            tensor<fp16, [1]> var_32_bias_0_to_fp16 = const()[name = tensor<string, []>("op_32_bias_0_to_fp16"), val = tensor<fp16, [1]>([0x0p+0])];
            tensor<fp16, [1, 1]> var_32_cast = linear(bias = var_32_bias_0_to_fp16, weight = neighborhood_agg_weight_to_fp16, x = input_1_cast);
            tensor<int32, [1]> neighborhood_score_axes_0 = const()[name = tensor<string, []>("neighborhood_score_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1]> neighborhood_score_cast = squeeze(axes = neighborhood_score_axes_0, x = var_32_cast);
            tensor<string, []> neighborhood_score_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("neighborhood_score_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> var_36 = const()[name = tensor<string, []>("op_36"), val = tensor<int32, []>(0)];
            tensor<bool, []> input_interleave_0 = const()[name = tensor<string, []>("input_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2]> input_cast = concat(axis = var_36, interleave = input_interleave_0, values = (reduce_max_0_cast, neighborhood_score_cast));
            tensor<fp16, [1, 2]> score_agg_weight_to_fp16 = const()[name = tensor<string, []>("score_agg_weight_to_fp16"), val = tensor<fp16, [1, 2]>([[0x1p+1, 0x1p+0]])];
            tensor<fp16, [1]> var_40_bias_0_to_fp16 = const()[name = tensor<string, []>("op_40_bias_0_to_fp16"), val = tensor<fp16, [1]>([0x0p+0])];
            tensor<fp16, [1]> var_40_cast = linear(bias = var_40_bias_0_to_fp16, weight = score_agg_weight_to_fp16, x = input_cast);
            tensor<string, []> var_40_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_40_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1]> var_40 = cast(dtype = var_40_cast_to_fp32_dtype_0, x = var_40_cast);
            tensor<fp32, [1]> neighborhood_score = cast(dtype = neighborhood_score_cast_to_fp32_dtype_0, x = neighborhood_score_cast);
            tensor<fp32, [1]> reduce_max_0 = cast(dtype = reduce_max_0_cast_to_fp32_dtype_0, x = reduce_max_0_cast);
        } -> (reduce_max_0, neighborhood_score, var_40);
}