program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.2.1"}, {"coremlc-version", "3520.2.1"}, {"coremltools-component-torch", "2.0.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.1.2"}, {"mldb_token", "mldb-wrxg5qmgo8"}})]
{
    func main<ios15>(tensor<fp32, [1, 4]> feature_vector) {
            tensor<fp32, [128]> model_bn1_running_var = const()[name = tensor<string, []>("model_bn1_running_var"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [128]> model_bn1_running_mean = const()[name = tensor<string, []>("model_bn1_running_mean"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640)))];
            tensor<fp32, [128]> model_bn1_bias = const()[name = tensor<string, []>("model_bn1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1216)))];
            tensor<fp32, [128]> model_bn1_weight = const()[name = tensor<string, []>("model_bn1_weight"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1792)))];
            tensor<fp32, []> var_5 = const()[name = tensor<string, []>("op_5"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<string, []> feature_vector_to_fp16_dtype_0 = const()[name = tensor<string, []>("feature_vector_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [128, 4]> model_l1_weight_to_fp16 = const()[name = tensor<string, []>("model_l1_weight_to_fp16"), val = tensor<fp16, [128, 4]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2368)))];
            tensor<fp16, [128]> model_l1_bias_to_fp16 = const()[name = tensor<string, []>("model_l1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3456)))];
            tensor<fp16, [1, 4]> cast_3 = cast(dtype = feature_vector_to_fp16_dtype_0, x = feature_vector)[name = tensor<string, []>("cast_3")];
            tensor<fp16, [1, 128]> linear_0_cast_fp16 = linear(bias = model_l1_bias_to_fp16, weight = model_l1_weight_to_fp16, x = cast_3)[name = tensor<string, []>("linear_0_cast_fp16")];
            tensor<int32, [1]> input_3_rank2_expansion_axes_0 = const()[name = tensor<string, []>("input_3_rank2_expansion_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 128, 1]> input_3_rank2_expansion_cast_fp16 = expand_dims(axes = input_3_rank2_expansion_axes_0, x = linear_0_cast_fp16)[name = tensor<string, []>("input_3_rank2_expansion_cast_fp16")];
            tensor<string, []> input_3_rank2_expansion_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("input_3_rank2_expansion_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 128, 1]> cast_2 = cast(dtype = input_3_rank2_expansion_cast_fp16_to_fp32_dtype_0, x = input_3_rank2_expansion_cast_fp16)[name = tensor<string, []>("cast_2")];
            tensor<fp32, [1, 128, 1]> input_3_batch_norm_1d = batch_norm(beta = model_bn1_bias, epsilon = var_5, gamma = model_bn1_weight, mean = model_bn1_running_mean, variance = model_bn1_running_var, x = cast_2)[name = tensor<string, []>("input_3_batch_norm_1d")];
            tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<string, []> input_3_batch_norm_1d_to_fp16_dtype_0 = const()[name = tensor<string, []>("input_3_batch_norm_1d_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 128, 1]> cast_1 = cast(dtype = input_3_batch_norm_1d_to_fp16_dtype_0, x = input_3_batch_norm_1d)[name = tensor<string, []>("cast_1")];
            tensor<fp16, [1, 128]> input_3_cast_fp16 = squeeze(axes = input_3_axes_0, x = cast_1)[name = tensor<string, []>("input_3_cast_fp16")];
            tensor<fp16, [1, 128]> input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
            tensor<fp16, [64, 128]> model_l2_weight_to_fp16 = const()[name = tensor<string, []>("model_l2_weight_to_fp16"), val = tensor<fp16, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3776)))];
            tensor<fp16, [64]> model_l2_bias_to_fp16 = const()[name = tensor<string, []>("model_l2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20224)))];
            tensor<fp16, [1, 64]> linear_1_cast_fp16 = linear(bias = model_l2_bias_to_fp16, weight = model_l2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
            tensor<int32, [1]> input_9_rank2_expansion_axes_0 = const()[name = tensor<string, []>("input_9_rank2_expansion_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 64, 1]> input_9_rank2_expansion_cast_fp16 = expand_dims(axes = input_9_rank2_expansion_axes_0, x = linear_1_cast_fp16)[name = tensor<string, []>("input_9_rank2_expansion_cast_fp16")];
            tensor<fp16, [64]> model_bn2_running_mean_to_fp16 = const()[name = tensor<string, []>("model_bn2_running_mean_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20416)))];
            tensor<fp16, [64]> model_bn2_running_var_to_fp16 = const()[name = tensor<string, []>("model_bn2_running_var_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20608)))];
            tensor<fp16, [64]> model_bn2_weight_to_fp16 = const()[name = tensor<string, []>("model_bn2_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20800)))];
            tensor<fp16, [64]> model_bn2_bias_to_fp16 = const()[name = tensor<string, []>("model_bn2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20992)))];
            tensor<fp16, []> var_5_to_fp16 = const()[name = tensor<string, []>("op_5_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 64, 1]> input_9_batch_norm_1d_cast_fp16 = batch_norm(beta = model_bn2_bias_to_fp16, epsilon = var_5_to_fp16, gamma = model_bn2_weight_to_fp16, mean = model_bn2_running_mean_to_fp16, variance = model_bn2_running_var_to_fp16, x = input_9_rank2_expansion_cast_fp16)[name = tensor<string, []>("input_9_batch_norm_1d_cast_fp16")];
            tensor<int32, [1]> input_9_axes_0 = const()[name = tensor<string, []>("input_9_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 64]> input_9_cast_fp16 = squeeze(axes = input_9_axes_0, x = input_9_batch_norm_1d_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
            tensor<fp16, [1, 64]> input_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
            tensor<fp16, [3, 64]> model_l3_weight_to_fp16 = const()[name = tensor<string, []>("model_l3_weight_to_fp16"), val = tensor<fp16, [3, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21184)))];
            tensor<fp16, [3]> model_l3_bias_to_fp16 = const()[name = tensor<string, []>("model_l3_bias_to_fp16"), val = tensor<fp16, [3]>([0x1.09cp-6, 0x1.268p-6, 0x1.2d8p-6])];
            tensor<fp16, [1, 3]> linear_2_cast_fp16 = linear(bias = model_l3_bias_to_fp16, weight = model_l3_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
            tensor<string, []> linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("linear_2_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 3]> anchor_points = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = tensor<string, []>("cast_0")];
        } -> (anchor_points);
}