program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.2.1"}, {"coremlc-version", "3520.2.1"}, {"mldb_token", "mldb-rdg2nkxg5z"}})]
{
    func main<ios16>(tensor<fp32, [1, 512, 1, 256]> inp) {
            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<string, []> inp_to_fp16_dtype_0 = const()[name = tensor<string, []>("inp_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 512, 1, 256]> cast_1 = cast(dtype = inp_to_fp16_dtype_0, x = inp)[name = tensor<string, []>("cast_1")];
            tensor<fp16, [1, 512, 256]> input_1_cast_fp16 = squeeze(axes = input_1_axes_0, x = cast_1)[name = tensor<string, []>("input_1_cast_fp16")];
            tensor<int32, []> var_14 = const()[name = tensor<string, []>("op_14"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_17 = const()[name = tensor<string, []>("op_17"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_19 = const()[name = tensor<string, []>("op_19"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_5_pad_type_0 = const()[name = tensor<string, []>("input_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 512, 1]> l_conv_1_cross_1_weight_to_fp16_affine_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("l_conv_1_cross_1_weight_to_fp16_affine_quantized"), quantized_data = tensor<int8, [256, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131520))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131200)))];
            tensor<fp16, [256]> l_conv_1_cross_1_bias_to_fp16 = const()[name = tensor<string, []>("l_conv_1_cross_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132096)))];
            tensor<fp16, [1, 256, 256]> input_5_cast_fp16 = conv(bias = l_conv_1_cross_1_bias_to_fp16, dilations = var_19, groups = var_14, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = var_17, weight = l_conv_1_cross_1_weight_to_fp16_affine_quantized, x = input_1_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
            tensor<fp16, [1, 256, 256]> input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
            tensor<int32, []> var_26 = const()[name = tensor<string, []>("op_26"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_29 = const()[name = tensor<string, []>("op_29"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_31 = const()[name = tensor<string, []>("op_31"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [128, 256, 5]> l_conv_1_weight_to_fp16_affine_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("l_conv_1_weight_to_fp16_affine_quantized"), quantized_data = tensor<int8, [128, 256, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132672))), scale = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(296768))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(296576)))];
            tensor<fp16, [128]> l_conv_1_bias_to_fp16 = const()[name = tensor<string, []>("l_conv_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297088)))];
            tensor<fp16, [1, 128, 252]> input_9_cast_fp16 = conv(bias = l_conv_1_bias_to_fp16, dilations = var_31, groups = var_26, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_29, weight = l_conv_1_weight_to_fp16_affine_quantized, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
            tensor<fp16, [1, 128, 252]> input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
            tensor<int32, [1]> var_39 = const()[name = tensor<string, []>("op_39"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> var_40 = const()[name = tensor<string, []>("op_40"), val = tensor<int32, [1]>([5])];
            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<bool, []> input_13_ceil_mode_0 = const()[name = tensor<string, []>("input_13_ceil_mode_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 128, 50]> input_13_cast_fp16 = max_pool(ceil_mode = input_13_ceil_mode_0, kernel_sizes = var_39, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_40, x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
            tensor<int32, []> var_47 = const()[name = tensor<string, []>("op_47"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_50 = const()[name = tensor<string, []>("op_50"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_52 = const()[name = tensor<string, []>("op_52"), val = tensor<int32, [1]>([1])];
            tensor<string, []> input_15_pad_type_0 = const()[name = tensor<string, []>("input_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [64, 128, 5]> l_conv_2_weight_to_fp16_affine_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("l_conv_2_weight_to_fp16_affine_quantized"), quantized_data = tensor<int8, [64, 128, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297408))), scale = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338560))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338432)))];
            tensor<fp16, [64]> l_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("l_conv_2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338752)))];
            tensor<fp16, [1, 64, 46]> input_15_cast_fp16 = conv(bias = l_conv_2_bias_to_fp16, dilations = var_52, groups = var_47, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_50, weight = l_conv_2_weight_to_fp16_affine_quantized, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
            tensor<fp16, [1, 64, 46]> input_17_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
            tensor<int32, [1]> var_60 = const()[name = tensor<string, []>("op_60"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> var_61 = const()[name = tensor<string, []>("op_61"), val = tensor<int32, [1]>([5])];
            tensor<string, []> x_pad_type_0 = const()[name = tensor<string, []>("x_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_pad_0 = const()[name = tensor<string, []>("x_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<bool, []> x_ceil_mode_0 = const()[name = tensor<string, []>("x_ceil_mode_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 64, 9]> x_cast_fp16 = max_pool(ceil_mode = x_ceil_mode_0, kernel_sizes = var_60, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_61, x = input_17_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
            tensor<int32, [2]> var_67 = const()[name = tensor<string, []>("op_67"), val = tensor<int32, [2]>([-1, 576])];
            tensor<fp16, [1, 576]> input_19_cast_fp16 = reshape(shape = var_67, x = x_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")];
            tensor<fp16, [2, 576]> l_fc_1_weight_to_fp16 = const()[name = tensor<string, []>("l_fc_1_weight_to_fp16"), val = tensor<fp16, [2, 576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338944)))];
            tensor<fp16, [2]> l_fc_1_bias_to_fp16 = const()[name = tensor<string, []>("l_fc_1_bias_to_fp16"), val = tensor<fp16, [2]>([-0x1.d0cp-5, 0x1.528p-5])];
            tensor<fp16, [1, 2]> linear_0_cast_fp16 = linear(bias = l_fc_1_bias_to_fp16, weight = l_fc_1_weight_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
            tensor<string, []> linear_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("linear_0_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 2]> linear_0 = cast(dtype = linear_0_cast_fp16_to_fp32_dtype_0, x = linear_0_cast_fp16)[name = tensor<string, []>("cast_0")];
        } -> (linear_0);
}