program(1.3) [buildInfo = dict({{"coremlc-component-MIL", "3400.43.1"}, {"coremlc-version", "3400.58.2"}, {"coremltools-component-torch", "2.4.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.0"}})] { func main(tensor audio_data, state> k_cache1, state> k_cache2, state> v_cache1, state> v_cache2) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"audio_data", [1, 1, 512]}}), ("RangeDims", {{"audio_data", [[1, 1], [1, 1500], [512, 512]]}})))] { tensor dummy = identity(x = audio_data)[name = string("identity_0")]; tensor read_state_0 = read_state(input = k_cache1)[name = string("read_state_0")]; tensor concat_0 = const()[name = string("concat_0"), val = tensor([0, 0, 0, 0])]; tensor concat_1 = const()[name = string("concat_1"), val = tensor([0, 0, 0, 0])]; tensor k_cache1_internal_tensor_assign_1_stride_0 = const()[name = string("k_cache1_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1])]; tensor k_cache1_internal_tensor_assign_1_begin_mask_0 = const()[name = string("k_cache1_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, false, false, false])]; tensor k_cache1_internal_tensor_assign_1_end_mask_0 = const()[name = string("k_cache1_internal_tensor_assign_1_end_mask_0"), val = tensor([true, true, true, true])]; tensor k_cache1_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("k_cache1_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([false, false, false, false])]; tensor const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; tensor k_cache1_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_0, begin_mask = k_cache1_internal_tensor_assign_1_begin_mask_0, end = concat_1, end_mask = k_cache1_internal_tensor_assign_1_end_mask_0, squeeze_mask = k_cache1_internal_tensor_assign_1_squeeze_mask_0, stride = k_cache1_internal_tensor_assign_1_stride_0, update = const_0_to_fp16, x = read_state_0)[name = string("k_cache1_internal_tensor_assign_1_cast_fp16")]; write_state(data = k_cache1_internal_tensor_assign_1_cast_fp16, input = k_cache1)[name = string("coreml_update_state_14_write_state")]; tensor read_state_1 = read_state(input = v_cache1)[name = string("read_state_1")]; tensor concat_2 = const()[name = string("concat_2"), val = tensor([0, 0, 0, 0])]; tensor concat_3 = const()[name = string("concat_3"), val = tensor([0, 0, 0, 0])]; tensor v_cache1_internal_tensor_assign_1_stride_0 = const()[name = string("v_cache1_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1])]; tensor v_cache1_internal_tensor_assign_1_begin_mask_0 = const()[name = string("v_cache1_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, false, false, false])]; tensor v_cache1_internal_tensor_assign_1_end_mask_0 = const()[name = string("v_cache1_internal_tensor_assign_1_end_mask_0"), val = tensor([true, true, true, true])]; tensor v_cache1_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("v_cache1_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([false, false, false, false])]; tensor v_cache1_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_2, begin_mask = v_cache1_internal_tensor_assign_1_begin_mask_0, end = concat_3, end_mask = v_cache1_internal_tensor_assign_1_end_mask_0, squeeze_mask = v_cache1_internal_tensor_assign_1_squeeze_mask_0, stride = v_cache1_internal_tensor_assign_1_stride_0, update = const_0_to_fp16, x = read_state_1)[name = string("v_cache1_internal_tensor_assign_1_cast_fp16")]; write_state(data = v_cache1_internal_tensor_assign_1_cast_fp16, input = v_cache1)[name = string("coreml_update_state_15_write_state")]; tensor read_state_2 = read_state(input = k_cache2)[name = string("read_state_2")]; tensor read_state_3 = read_state(input = v_cache2)[name = string("read_state_3")]; tensor var_79_to_fp16 = const()[name = string("op_79_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2752640)))]; tensor linear_0_bias_0_to_fp16 = const()[name = string("linear_0_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3276992)))]; tensor linear_0_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_79_to_fp16, x = audio_data)[name = string("linear_0_cast_fp16")]; tensor var_83_to_fp16 = const()[name = string("op_83_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3278080)))]; tensor var_84_to_fp16 = const()[name = string("op_84_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3802432)))]; tensor linear_1_cast_fp16 = linear(bias = var_84_to_fp16, weight = var_83_to_fp16, x = audio_data)[name = string("linear_1_cast_fp16")]; tensor var_86_shape_cast_fp16 = shape(x = linear_0_cast_fp16)[name = string("op_86_shape_cast_fp16")]; int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)]; int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)]; bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)]; string var_86_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_86_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; uint16 select_0_to_uint16 = const()[name = string("select_0_to_uint16"), val = uint16(1)]; tensor var_86_shape_cast_fp16_to_int16 = cast(dtype = var_86_shape_cast_fp16_to_int16_dtype_0, x = var_86_shape_cast_fp16)[name = string("cast_43")]; int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = select_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_86_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")]; string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_11_axes_0 = const()[name = string("expand_dims_11_axes_0"), val = tensor([0])]; int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_42")]; tensor expand_dims_11 = expand_dims(axes = expand_dims_11_axes_0, x = gather_0_cast_uint16_to_int32)[name = string("expand_dims_11")]; tensor concat_5 = const()[name = string("concat_5"), val = tensor([0, 0, 0, 0])]; tensor concat_6_values0_0 = const()[name = string("concat_6_values0_0"), val = tensor([0])]; tensor concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor([0])]; tensor concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor([0])]; int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)]; bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)]; tensor concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (concat_6_values0_0, concat_6_values1_0, expand_dims_11, concat_6_values3_0))[name = string("concat_6")]; tensor k_cache2_internal_tensor_assign_1_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1])]; tensor k_cache2_internal_tensor_assign_1_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, false, false, false])]; tensor k_cache2_internal_tensor_assign_1_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, false, true])]; tensor k_cache2_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor k_cache2_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = k_cache2_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = k_cache2_internal_tensor_assign_1_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_1_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_1_stride_0, update = linear_0_cast_fp16, x = read_state_2)[name = string("k_cache2_internal_tensor_assign_1_cast_fp16")]; write_state(data = k_cache2_internal_tensor_assign_1_cast_fp16, input = k_cache2)[name = string("coreml_update_state_16_write_state")]; tensor coreml_update_state_16 = read_state(input = k_cache2)[name = string("coreml_update_state_16")]; tensor var_91_shape_cast_fp16 = shape(x = linear_1_cast_fp16)[name = string("op_91_shape_cast_fp16")]; int32 gather_1_axis_0 = const()[name = string("gather_1_axis_0"), val = int32(0)]; int32 gather_1_batch_dims_0 = const()[name = string("gather_1_batch_dims_0"), val = int32(0)]; bool gather_1_validate_indices_0 = const()[name = string("gather_1_validate_indices_0"), val = bool(false)]; string var_91_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_91_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_1_to_uint16 = const()[name = string("select_1_to_uint16"), val = uint16(1)]; tensor var_91_shape_cast_fp16_to_uint16 = cast(dtype = var_91_shape_cast_fp16_to_uint16_dtype_0, x = var_91_shape_cast_fp16)[name = string("cast_41")]; uint16 gather_1_cast_uint16 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = select_1_to_uint16, validate_indices = gather_1_validate_indices_0, x = var_91_shape_cast_fp16_to_uint16)[name = string("gather_1_cast_uint16")]; string gather_1_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_1_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_15_axes_0 = const()[name = string("expand_dims_15_axes_0"), val = tensor([0])]; int32 gather_1_cast_uint16_to_int32 = cast(dtype = gather_1_cast_uint16_to_int32_dtype_0, x = gather_1_cast_uint16)[name = string("cast_40")]; tensor expand_dims_15 = expand_dims(axes = expand_dims_15_axes_0, x = gather_1_cast_uint16_to_int32)[name = string("expand_dims_15")]; tensor concat_8 = const()[name = string("concat_8"), val = tensor([0, 0, 0, 0])]; tensor concat_9_values0_0 = const()[name = string("concat_9_values0_0"), val = tensor([0])]; tensor concat_9_values1_0 = const()[name = string("concat_9_values1_0"), val = tensor([0])]; tensor concat_9_values3_0 = const()[name = string("concat_9_values3_0"), val = tensor([0])]; int32 concat_9_axis_0 = const()[name = string("concat_9_axis_0"), val = int32(0)]; bool concat_9_interleave_0 = const()[name = string("concat_9_interleave_0"), val = bool(false)]; tensor concat_9 = concat(axis = concat_9_axis_0, interleave = concat_9_interleave_0, values = (concat_9_values0_0, concat_9_values1_0, expand_dims_15, concat_9_values3_0))[name = string("concat_9")]; tensor v_cache2_internal_tensor_assign_1_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1])]; tensor v_cache2_internal_tensor_assign_1_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, false, false, false])]; tensor v_cache2_internal_tensor_assign_1_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, false, true])]; tensor v_cache2_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor v_cache2_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_8, begin_mask = v_cache2_internal_tensor_assign_1_begin_mask_0, end = concat_9, end_mask = v_cache2_internal_tensor_assign_1_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_1_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_1_stride_0, update = linear_1_cast_fp16, x = read_state_3)[name = string("v_cache2_internal_tensor_assign_1_cast_fp16")]; write_state(data = v_cache2_internal_tensor_assign_1_cast_fp16, input = v_cache2)[name = string("coreml_update_state_17_write_state")]; tensor coreml_update_state_17 = read_state(input = v_cache2)[name = string("coreml_update_state_17")]; tensor var_113_to_fp16 = const()[name = string("op_113_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3803520)))]; tensor linear_2_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_113_to_fp16, x = audio_data)[name = string("linear_2_cast_fp16")]; tensor var_117_to_fp16 = const()[name = string("op_117_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4327872)))]; tensor var_118_to_fp16 = const()[name = string("op_118_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4852224)))]; tensor linear_3_cast_fp16 = linear(bias = var_118_to_fp16, weight = var_117_to_fp16, x = audio_data)[name = string("linear_3_cast_fp16")]; tensor var_120_shape_cast_fp16 = shape(x = linear_2_cast_fp16)[name = string("op_120_shape_cast_fp16")]; int32 gather_2_axis_0 = const()[name = string("gather_2_axis_0"), val = int32(0)]; int32 gather_2_batch_dims_0 = const()[name = string("gather_2_batch_dims_0"), val = int32(0)]; bool gather_2_validate_indices_0 = const()[name = string("gather_2_validate_indices_0"), val = bool(false)]; string var_120_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_120_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_2_to_uint16 = const()[name = string("select_2_to_uint16"), val = uint16(1)]; tensor var_120_shape_cast_fp16_to_uint16 = cast(dtype = var_120_shape_cast_fp16_to_uint16_dtype_0, x = var_120_shape_cast_fp16)[name = string("cast_39")]; uint16 gather_2_cast_uint16 = gather(axis = gather_2_axis_0, batch_dims = gather_2_batch_dims_0, indices = select_2_to_uint16, validate_indices = gather_2_validate_indices_0, x = var_120_shape_cast_fp16_to_uint16)[name = string("gather_2_cast_uint16")]; string gather_2_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_2_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_19_axes_0 = const()[name = string("expand_dims_19_axes_0"), val = tensor([0])]; int32 gather_2_cast_uint16_to_int32 = cast(dtype = gather_2_cast_uint16_to_int32_dtype_0, x = gather_2_cast_uint16)[name = string("cast_38")]; tensor expand_dims_19 = expand_dims(axes = expand_dims_19_axes_0, x = gather_2_cast_uint16_to_int32)[name = string("expand_dims_19")]; tensor concat_11 = const()[name = string("concat_11"), val = tensor([1, 0, 0, 0])]; tensor concat_12_values0_0 = const()[name = string("concat_12_values0_0"), val = tensor([0])]; tensor concat_12_values1_0 = const()[name = string("concat_12_values1_0"), val = tensor([0])]; tensor concat_12_values3_0 = const()[name = string("concat_12_values3_0"), val = tensor([0])]; int32 concat_12_axis_0 = const()[name = string("concat_12_axis_0"), val = int32(0)]; bool concat_12_interleave_0 = const()[name = string("concat_12_interleave_0"), val = bool(false)]; tensor concat_12 = concat(axis = concat_12_axis_0, interleave = concat_12_interleave_0, values = (concat_12_values0_0, concat_12_values1_0, expand_dims_19, concat_12_values3_0))[name = string("concat_12")]; tensor k_cache2_internal_tensor_assign_2_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1])]; tensor k_cache2_internal_tensor_assign_2_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, false, false, false])]; tensor k_cache2_internal_tensor_assign_2_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, false, true])]; tensor k_cache2_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor k_cache2_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_11, begin_mask = k_cache2_internal_tensor_assign_2_begin_mask_0, end = concat_12, end_mask = k_cache2_internal_tensor_assign_2_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_2_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_2_stride_0, update = linear_2_cast_fp16, x = coreml_update_state_16)[name = string("k_cache2_internal_tensor_assign_2_cast_fp16")]; write_state(data = k_cache2_internal_tensor_assign_2_cast_fp16, input = k_cache2)[name = string("coreml_update_state_18_write_state")]; tensor coreml_update_state_18 = read_state(input = k_cache2)[name = string("coreml_update_state_18")]; tensor var_125_shape_cast_fp16 = shape(x = linear_3_cast_fp16)[name = string("op_125_shape_cast_fp16")]; int32 gather_3_axis_0 = const()[name = string("gather_3_axis_0"), val = int32(0)]; int32 gather_3_batch_dims_0 = const()[name = string("gather_3_batch_dims_0"), val = int32(0)]; bool gather_3_validate_indices_0 = const()[name = string("gather_3_validate_indices_0"), val = bool(false)]; string var_125_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_125_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_3_to_uint16 = const()[name = string("select_3_to_uint16"), val = uint16(1)]; tensor var_125_shape_cast_fp16_to_uint16 = cast(dtype = var_125_shape_cast_fp16_to_uint16_dtype_0, x = var_125_shape_cast_fp16)[name = string("cast_37")]; uint16 gather_3_cast_uint16 = gather(axis = gather_3_axis_0, batch_dims = gather_3_batch_dims_0, indices = select_3_to_uint16, validate_indices = gather_3_validate_indices_0, x = var_125_shape_cast_fp16_to_uint16)[name = string("gather_3_cast_uint16")]; string gather_3_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_3_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_23_axes_0 = const()[name = string("expand_dims_23_axes_0"), val = tensor([0])]; int32 gather_3_cast_uint16_to_int32 = cast(dtype = gather_3_cast_uint16_to_int32_dtype_0, x = gather_3_cast_uint16)[name = string("cast_36")]; tensor expand_dims_23 = expand_dims(axes = expand_dims_23_axes_0, x = gather_3_cast_uint16_to_int32)[name = string("expand_dims_23")]; tensor concat_14 = const()[name = string("concat_14"), val = tensor([1, 0, 0, 0])]; tensor concat_15_values0_0 = const()[name = string("concat_15_values0_0"), val = tensor([0])]; tensor concat_15_values1_0 = const()[name = string("concat_15_values1_0"), val = tensor([0])]; tensor concat_15_values3_0 = const()[name = string("concat_15_values3_0"), val = tensor([0])]; int32 concat_15_axis_0 = const()[name = string("concat_15_axis_0"), val = int32(0)]; bool concat_15_interleave_0 = const()[name = string("concat_15_interleave_0"), val = bool(false)]; tensor concat_15 = concat(axis = concat_15_axis_0, interleave = concat_15_interleave_0, values = (concat_15_values0_0, concat_15_values1_0, expand_dims_23, concat_15_values3_0))[name = string("concat_15")]; tensor v_cache2_internal_tensor_assign_2_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1])]; tensor v_cache2_internal_tensor_assign_2_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, false, false, false])]; tensor v_cache2_internal_tensor_assign_2_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, false, true])]; tensor v_cache2_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor v_cache2_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_14, begin_mask = v_cache2_internal_tensor_assign_2_begin_mask_0, end = concat_15, end_mask = v_cache2_internal_tensor_assign_2_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_2_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_2_stride_0, update = linear_3_cast_fp16, x = coreml_update_state_17)[name = string("v_cache2_internal_tensor_assign_2_cast_fp16")]; write_state(data = v_cache2_internal_tensor_assign_2_cast_fp16, input = v_cache2)[name = string("coreml_update_state_19_write_state")]; tensor coreml_update_state_19 = read_state(input = v_cache2)[name = string("coreml_update_state_19")]; tensor var_147_to_fp16 = const()[name = string("op_147_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4853312)))]; tensor linear_4_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_147_to_fp16, x = audio_data)[name = string("linear_4_cast_fp16")]; tensor var_151_to_fp16 = const()[name = string("op_151_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5377664)))]; tensor var_152_to_fp16 = const()[name = string("op_152_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5902016)))]; tensor linear_5_cast_fp16 = linear(bias = var_152_to_fp16, weight = var_151_to_fp16, x = audio_data)[name = string("linear_5_cast_fp16")]; tensor var_154_shape_cast_fp16 = shape(x = linear_4_cast_fp16)[name = string("op_154_shape_cast_fp16")]; int32 gather_4_axis_0 = const()[name = string("gather_4_axis_0"), val = int32(0)]; int32 gather_4_batch_dims_0 = const()[name = string("gather_4_batch_dims_0"), val = int32(0)]; bool gather_4_validate_indices_0 = const()[name = string("gather_4_validate_indices_0"), val = bool(false)]; string var_154_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_154_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_4_to_uint16 = const()[name = string("select_4_to_uint16"), val = uint16(1)]; tensor var_154_shape_cast_fp16_to_uint16 = cast(dtype = var_154_shape_cast_fp16_to_uint16_dtype_0, x = var_154_shape_cast_fp16)[name = string("cast_35")]; uint16 gather_4_cast_uint16 = gather(axis = gather_4_axis_0, batch_dims = gather_4_batch_dims_0, indices = select_4_to_uint16, validate_indices = gather_4_validate_indices_0, x = var_154_shape_cast_fp16_to_uint16)[name = string("gather_4_cast_uint16")]; string gather_4_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_4_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_27_axes_0 = const()[name = string("expand_dims_27_axes_0"), val = tensor([0])]; int32 gather_4_cast_uint16_to_int32 = cast(dtype = gather_4_cast_uint16_to_int32_dtype_0, x = gather_4_cast_uint16)[name = string("cast_34")]; tensor expand_dims_27 = expand_dims(axes = expand_dims_27_axes_0, x = gather_4_cast_uint16_to_int32)[name = string("expand_dims_27")]; tensor concat_17 = const()[name = string("concat_17"), val = tensor([2, 0, 0, 0])]; tensor concat_18_values0_0 = const()[name = string("concat_18_values0_0"), val = tensor([0])]; tensor concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor([0])]; tensor concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor([0])]; int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)]; bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)]; tensor concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (concat_18_values0_0, concat_18_values1_0, expand_dims_27, concat_18_values3_0))[name = string("concat_18")]; tensor k_cache2_internal_tensor_assign_3_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_3_stride_0"), val = tensor([1, 1, 1, 1])]; tensor k_cache2_internal_tensor_assign_3_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_3_begin_mask_0"), val = tensor([false, false, false, false])]; tensor k_cache2_internal_tensor_assign_3_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_3_end_mask_0"), val = tensor([false, true, false, true])]; tensor k_cache2_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_3_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor k_cache2_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_17, begin_mask = k_cache2_internal_tensor_assign_3_begin_mask_0, end = concat_18, end_mask = k_cache2_internal_tensor_assign_3_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_3_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_3_stride_0, update = linear_4_cast_fp16, x = coreml_update_state_18)[name = string("k_cache2_internal_tensor_assign_3_cast_fp16")]; write_state(data = k_cache2_internal_tensor_assign_3_cast_fp16, input = k_cache2)[name = string("coreml_update_state_20_write_state")]; tensor coreml_update_state_20 = read_state(input = k_cache2)[name = string("coreml_update_state_20")]; tensor var_159_shape_cast_fp16 = shape(x = linear_5_cast_fp16)[name = string("op_159_shape_cast_fp16")]; int32 gather_5_axis_0 = const()[name = string("gather_5_axis_0"), val = int32(0)]; int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; string var_159_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_159_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_5_to_uint16 = const()[name = string("select_5_to_uint16"), val = uint16(1)]; tensor var_159_shape_cast_fp16_to_uint16 = cast(dtype = var_159_shape_cast_fp16_to_uint16_dtype_0, x = var_159_shape_cast_fp16)[name = string("cast_33")]; uint16 gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = select_5_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_159_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16")]; string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_31_axes_0 = const()[name = string("expand_dims_31_axes_0"), val = tensor([0])]; int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = string("cast_32")]; tensor expand_dims_31 = expand_dims(axes = expand_dims_31_axes_0, x = gather_5_cast_uint16_to_int32)[name = string("expand_dims_31")]; tensor concat_20 = const()[name = string("concat_20"), val = tensor([2, 0, 0, 0])]; tensor concat_21_values0_0 = const()[name = string("concat_21_values0_0"), val = tensor([0])]; tensor concat_21_values1_0 = const()[name = string("concat_21_values1_0"), val = tensor([0])]; tensor concat_21_values3_0 = const()[name = string("concat_21_values3_0"), val = tensor([0])]; int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(0)]; bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)]; tensor concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (concat_21_values0_0, concat_21_values1_0, expand_dims_31, concat_21_values3_0))[name = string("concat_21")]; tensor v_cache2_internal_tensor_assign_3_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_3_stride_0"), val = tensor([1, 1, 1, 1])]; tensor v_cache2_internal_tensor_assign_3_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_3_begin_mask_0"), val = tensor([false, false, false, false])]; tensor v_cache2_internal_tensor_assign_3_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_3_end_mask_0"), val = tensor([false, true, false, true])]; tensor v_cache2_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_3_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor v_cache2_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_20, begin_mask = v_cache2_internal_tensor_assign_3_begin_mask_0, end = concat_21, end_mask = v_cache2_internal_tensor_assign_3_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_3_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_3_stride_0, update = linear_5_cast_fp16, x = coreml_update_state_19)[name = string("v_cache2_internal_tensor_assign_3_cast_fp16")]; write_state(data = v_cache2_internal_tensor_assign_3_cast_fp16, input = v_cache2)[name = string("coreml_update_state_21_write_state")]; tensor coreml_update_state_21 = read_state(input = v_cache2)[name = string("coreml_update_state_21")]; tensor var_181_to_fp16 = const()[name = string("op_181_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5903104)))]; tensor linear_6_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_181_to_fp16, x = audio_data)[name = string("linear_6_cast_fp16")]; tensor var_185_to_fp16 = const()[name = string("op_185_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6427456)))]; tensor var_186_to_fp16 = const()[name = string("op_186_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6951808)))]; tensor linear_7_cast_fp16 = linear(bias = var_186_to_fp16, weight = var_185_to_fp16, x = audio_data)[name = string("linear_7_cast_fp16")]; tensor var_188_shape_cast_fp16 = shape(x = linear_6_cast_fp16)[name = string("op_188_shape_cast_fp16")]; int32 gather_6_axis_0 = const()[name = string("gather_6_axis_0"), val = int32(0)]; int32 gather_6_batch_dims_0 = const()[name = string("gather_6_batch_dims_0"), val = int32(0)]; bool gather_6_validate_indices_0 = const()[name = string("gather_6_validate_indices_0"), val = bool(false)]; string var_188_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_188_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_6_to_uint16 = const()[name = string("select_6_to_uint16"), val = uint16(1)]; tensor var_188_shape_cast_fp16_to_uint16 = cast(dtype = var_188_shape_cast_fp16_to_uint16_dtype_0, x = var_188_shape_cast_fp16)[name = string("cast_31")]; uint16 gather_6_cast_uint16 = gather(axis = gather_6_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_6_to_uint16, validate_indices = gather_6_validate_indices_0, x = var_188_shape_cast_fp16_to_uint16)[name = string("gather_6_cast_uint16")]; string gather_6_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_6_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_35_axes_0 = const()[name = string("expand_dims_35_axes_0"), val = tensor([0])]; int32 gather_6_cast_uint16_to_int32 = cast(dtype = gather_6_cast_uint16_to_int32_dtype_0, x = gather_6_cast_uint16)[name = string("cast_30")]; tensor expand_dims_35 = expand_dims(axes = expand_dims_35_axes_0, x = gather_6_cast_uint16_to_int32)[name = string("expand_dims_35")]; tensor concat_23 = const()[name = string("concat_23"), val = tensor([3, 0, 0, 0])]; tensor concat_24_values0_0 = const()[name = string("concat_24_values0_0"), val = tensor([0])]; tensor concat_24_values1_0 = const()[name = string("concat_24_values1_0"), val = tensor([0])]; tensor concat_24_values3_0 = const()[name = string("concat_24_values3_0"), val = tensor([0])]; int32 concat_24_axis_0 = const()[name = string("concat_24_axis_0"), val = int32(0)]; bool concat_24_interleave_0 = const()[name = string("concat_24_interleave_0"), val = bool(false)]; tensor concat_24 = concat(axis = concat_24_axis_0, interleave = concat_24_interleave_0, values = (concat_24_values0_0, concat_24_values1_0, expand_dims_35, concat_24_values3_0))[name = string("concat_24")]; tensor k_cache2_internal_tensor_assign_4_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_4_stride_0"), val = tensor([1, 1, 1, 1])]; tensor k_cache2_internal_tensor_assign_4_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_4_begin_mask_0"), val = tensor([false, false, false, false])]; tensor k_cache2_internal_tensor_assign_4_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_4_end_mask_0"), val = tensor([false, true, false, true])]; tensor k_cache2_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_4_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor k_cache2_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_23, begin_mask = k_cache2_internal_tensor_assign_4_begin_mask_0, end = concat_24, end_mask = k_cache2_internal_tensor_assign_4_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_4_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_4_stride_0, update = linear_6_cast_fp16, x = coreml_update_state_20)[name = string("k_cache2_internal_tensor_assign_4_cast_fp16")]; write_state(data = k_cache2_internal_tensor_assign_4_cast_fp16, input = k_cache2)[name = string("coreml_update_state_22_write_state")]; tensor coreml_update_state_22 = read_state(input = k_cache2)[name = string("coreml_update_state_22")]; tensor var_193_shape_cast_fp16 = shape(x = linear_7_cast_fp16)[name = string("op_193_shape_cast_fp16")]; int32 gather_7_axis_0 = const()[name = string("gather_7_axis_0"), val = int32(0)]; int32 gather_7_batch_dims_0 = const()[name = string("gather_7_batch_dims_0"), val = int32(0)]; bool gather_7_validate_indices_0 = const()[name = string("gather_7_validate_indices_0"), val = bool(false)]; string var_193_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_193_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_7_to_uint16 = const()[name = string("select_7_to_uint16"), val = uint16(1)]; tensor var_193_shape_cast_fp16_to_uint16 = cast(dtype = var_193_shape_cast_fp16_to_uint16_dtype_0, x = var_193_shape_cast_fp16)[name = string("cast_29")]; uint16 gather_7_cast_uint16 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = select_7_to_uint16, validate_indices = gather_7_validate_indices_0, x = var_193_shape_cast_fp16_to_uint16)[name = string("gather_7_cast_uint16")]; string gather_7_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_7_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_39_axes_0 = const()[name = string("expand_dims_39_axes_0"), val = tensor([0])]; int32 gather_7_cast_uint16_to_int32 = cast(dtype = gather_7_cast_uint16_to_int32_dtype_0, x = gather_7_cast_uint16)[name = string("cast_28")]; tensor expand_dims_39 = expand_dims(axes = expand_dims_39_axes_0, x = gather_7_cast_uint16_to_int32)[name = string("expand_dims_39")]; tensor concat_26 = const()[name = string("concat_26"), val = tensor([3, 0, 0, 0])]; tensor concat_27_values0_0 = const()[name = string("concat_27_values0_0"), val = tensor([0])]; tensor concat_27_values1_0 = const()[name = string("concat_27_values1_0"), val = tensor([0])]; tensor concat_27_values3_0 = const()[name = string("concat_27_values3_0"), val = tensor([0])]; int32 concat_27_axis_0 = const()[name = string("concat_27_axis_0"), val = int32(0)]; bool concat_27_interleave_0 = const()[name = string("concat_27_interleave_0"), val = bool(false)]; tensor concat_27 = concat(axis = concat_27_axis_0, interleave = concat_27_interleave_0, values = (concat_27_values0_0, concat_27_values1_0, expand_dims_39, concat_27_values3_0))[name = string("concat_27")]; tensor v_cache2_internal_tensor_assign_4_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_4_stride_0"), val = tensor([1, 1, 1, 1])]; tensor v_cache2_internal_tensor_assign_4_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_4_begin_mask_0"), val = tensor([false, false, false, false])]; tensor v_cache2_internal_tensor_assign_4_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_4_end_mask_0"), val = tensor([false, true, false, true])]; tensor v_cache2_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_4_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor v_cache2_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_26, begin_mask = v_cache2_internal_tensor_assign_4_begin_mask_0, end = concat_27, end_mask = v_cache2_internal_tensor_assign_4_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_4_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_4_stride_0, update = linear_7_cast_fp16, x = coreml_update_state_21)[name = string("v_cache2_internal_tensor_assign_4_cast_fp16")]; write_state(data = v_cache2_internal_tensor_assign_4_cast_fp16, input = v_cache2)[name = string("coreml_update_state_23_write_state")]; tensor coreml_update_state_23 = read_state(input = v_cache2)[name = string("coreml_update_state_23")]; tensor var_215_to_fp16 = const()[name = string("op_215_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6952896)))]; tensor linear_8_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_215_to_fp16, x = audio_data)[name = string("linear_8_cast_fp16")]; tensor var_219_to_fp16 = const()[name = string("op_219_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7477248)))]; tensor var_220_to_fp16 = const()[name = string("op_220_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8001600)))]; tensor linear_9_cast_fp16 = linear(bias = var_220_to_fp16, weight = var_219_to_fp16, x = audio_data)[name = string("linear_9_cast_fp16")]; tensor var_222_shape_cast_fp16 = shape(x = linear_8_cast_fp16)[name = string("op_222_shape_cast_fp16")]; int32 gather_8_axis_0 = const()[name = string("gather_8_axis_0"), val = int32(0)]; int32 gather_8_batch_dims_0 = const()[name = string("gather_8_batch_dims_0"), val = int32(0)]; bool gather_8_validate_indices_0 = const()[name = string("gather_8_validate_indices_0"), val = bool(false)]; string var_222_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_222_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_8_to_uint16 = const()[name = string("select_8_to_uint16"), val = uint16(1)]; tensor var_222_shape_cast_fp16_to_uint16 = cast(dtype = var_222_shape_cast_fp16_to_uint16_dtype_0, x = var_222_shape_cast_fp16)[name = string("cast_27")]; uint16 gather_8_cast_uint16 = gather(axis = gather_8_axis_0, batch_dims = gather_8_batch_dims_0, indices = select_8_to_uint16, validate_indices = gather_8_validate_indices_0, x = var_222_shape_cast_fp16_to_uint16)[name = string("gather_8_cast_uint16")]; string gather_8_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_8_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_43_axes_0 = const()[name = string("expand_dims_43_axes_0"), val = tensor([0])]; int32 gather_8_cast_uint16_to_int32 = cast(dtype = gather_8_cast_uint16_to_int32_dtype_0, x = gather_8_cast_uint16)[name = string("cast_26")]; tensor expand_dims_43 = expand_dims(axes = expand_dims_43_axes_0, x = gather_8_cast_uint16_to_int32)[name = string("expand_dims_43")]; tensor concat_29 = const()[name = string("concat_29"), val = tensor([4, 0, 0, 0])]; tensor concat_30_values0_0 = const()[name = string("concat_30_values0_0"), val = tensor([0])]; tensor concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor([0])]; tensor concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor([0])]; int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)]; bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)]; tensor concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (concat_30_values0_0, concat_30_values1_0, expand_dims_43, concat_30_values3_0))[name = string("concat_30")]; tensor k_cache2_internal_tensor_assign_5_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_5_stride_0"), val = tensor([1, 1, 1, 1])]; tensor k_cache2_internal_tensor_assign_5_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_5_begin_mask_0"), val = tensor([false, false, false, false])]; tensor k_cache2_internal_tensor_assign_5_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_5_end_mask_0"), val = tensor([false, true, false, true])]; tensor k_cache2_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_5_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor k_cache2_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_29, begin_mask = k_cache2_internal_tensor_assign_5_begin_mask_0, end = concat_30, end_mask = k_cache2_internal_tensor_assign_5_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_5_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_5_stride_0, update = linear_8_cast_fp16, x = coreml_update_state_22)[name = string("k_cache2_internal_tensor_assign_5_cast_fp16")]; write_state(data = k_cache2_internal_tensor_assign_5_cast_fp16, input = k_cache2)[name = string("coreml_update_state_24_write_state")]; tensor coreml_update_state_24 = read_state(input = k_cache2)[name = string("coreml_update_state_24")]; tensor var_227_shape_cast_fp16 = shape(x = linear_9_cast_fp16)[name = string("op_227_shape_cast_fp16")]; int32 gather_9_axis_0 = const()[name = string("gather_9_axis_0"), val = int32(0)]; int32 gather_9_batch_dims_0 = const()[name = string("gather_9_batch_dims_0"), val = int32(0)]; bool gather_9_validate_indices_0 = const()[name = string("gather_9_validate_indices_0"), val = bool(false)]; string var_227_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_227_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_9_to_uint16 = const()[name = string("select_9_to_uint16"), val = uint16(1)]; tensor var_227_shape_cast_fp16_to_uint16 = cast(dtype = var_227_shape_cast_fp16_to_uint16_dtype_0, x = var_227_shape_cast_fp16)[name = string("cast_25")]; uint16 gather_9_cast_uint16 = gather(axis = gather_9_axis_0, batch_dims = gather_9_batch_dims_0, indices = select_9_to_uint16, validate_indices = gather_9_validate_indices_0, x = var_227_shape_cast_fp16_to_uint16)[name = string("gather_9_cast_uint16")]; string gather_9_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_9_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_47_axes_0 = const()[name = string("expand_dims_47_axes_0"), val = tensor([0])]; int32 gather_9_cast_uint16_to_int32 = cast(dtype = gather_9_cast_uint16_to_int32_dtype_0, x = gather_9_cast_uint16)[name = string("cast_24")]; tensor expand_dims_47 = expand_dims(axes = expand_dims_47_axes_0, x = gather_9_cast_uint16_to_int32)[name = string("expand_dims_47")]; tensor concat_32 = const()[name = string("concat_32"), val = tensor([4, 0, 0, 0])]; tensor concat_33_values0_0 = const()[name = string("concat_33_values0_0"), val = tensor([0])]; tensor concat_33_values1_0 = const()[name = string("concat_33_values1_0"), val = tensor([0])]; tensor concat_33_values3_0 = const()[name = string("concat_33_values3_0"), val = tensor([0])]; int32 concat_33_axis_0 = const()[name = string("concat_33_axis_0"), val = int32(0)]; bool concat_33_interleave_0 = const()[name = string("concat_33_interleave_0"), val = bool(false)]; tensor concat_33 = concat(axis = concat_33_axis_0, interleave = concat_33_interleave_0, values = (concat_33_values0_0, concat_33_values1_0, expand_dims_47, concat_33_values3_0))[name = string("concat_33")]; tensor v_cache2_internal_tensor_assign_5_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_5_stride_0"), val = tensor([1, 1, 1, 1])]; tensor v_cache2_internal_tensor_assign_5_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_5_begin_mask_0"), val = tensor([false, false, false, false])]; tensor v_cache2_internal_tensor_assign_5_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_5_end_mask_0"), val = tensor([false, true, false, true])]; tensor v_cache2_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_5_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor v_cache2_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_32, begin_mask = v_cache2_internal_tensor_assign_5_begin_mask_0, end = concat_33, end_mask = v_cache2_internal_tensor_assign_5_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_5_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_5_stride_0, update = linear_9_cast_fp16, x = coreml_update_state_23)[name = string("v_cache2_internal_tensor_assign_5_cast_fp16")]; write_state(data = v_cache2_internal_tensor_assign_5_cast_fp16, input = v_cache2)[name = string("coreml_update_state_25_write_state")]; tensor coreml_update_state_25 = read_state(input = v_cache2)[name = string("coreml_update_state_25")]; tensor var_249_to_fp16 = const()[name = string("op_249_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8002688)))]; tensor linear_10_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_249_to_fp16, x = audio_data)[name = string("linear_10_cast_fp16")]; tensor var_253_to_fp16 = const()[name = string("op_253_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8527040)))]; tensor var_254_to_fp16 = const()[name = string("op_254_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9051392)))]; tensor linear_11_cast_fp16 = linear(bias = var_254_to_fp16, weight = var_253_to_fp16, x = audio_data)[name = string("linear_11_cast_fp16")]; tensor var_256_shape_cast_fp16 = shape(x = linear_10_cast_fp16)[name = string("op_256_shape_cast_fp16")]; int32 gather_10_axis_0 = const()[name = string("gather_10_axis_0"), val = int32(0)]; int32 gather_10_batch_dims_0 = const()[name = string("gather_10_batch_dims_0"), val = int32(0)]; bool gather_10_validate_indices_0 = const()[name = string("gather_10_validate_indices_0"), val = bool(false)]; string var_256_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_256_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_10_to_uint16 = const()[name = string("select_10_to_uint16"), val = uint16(1)]; tensor var_256_shape_cast_fp16_to_uint16 = cast(dtype = var_256_shape_cast_fp16_to_uint16_dtype_0, x = var_256_shape_cast_fp16)[name = string("cast_23")]; uint16 gather_10_cast_uint16 = gather(axis = gather_10_axis_0, batch_dims = gather_10_batch_dims_0, indices = select_10_to_uint16, validate_indices = gather_10_validate_indices_0, x = var_256_shape_cast_fp16_to_uint16)[name = string("gather_10_cast_uint16")]; string gather_10_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_10_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_51_axes_0 = const()[name = string("expand_dims_51_axes_0"), val = tensor([0])]; int32 gather_10_cast_uint16_to_int32 = cast(dtype = gather_10_cast_uint16_to_int32_dtype_0, x = gather_10_cast_uint16)[name = string("cast_22")]; tensor expand_dims_51 = expand_dims(axes = expand_dims_51_axes_0, x = gather_10_cast_uint16_to_int32)[name = string("expand_dims_51")]; tensor concat_35 = const()[name = string("concat_35"), val = tensor([5, 0, 0, 0])]; tensor concat_36_values0_0 = const()[name = string("concat_36_values0_0"), val = tensor([0])]; tensor concat_36_values1_0 = const()[name = string("concat_36_values1_0"), val = tensor([0])]; tensor concat_36_values3_0 = const()[name = string("concat_36_values3_0"), val = tensor([0])]; int32 concat_36_axis_0 = const()[name = string("concat_36_axis_0"), val = int32(0)]; bool concat_36_interleave_0 = const()[name = string("concat_36_interleave_0"), val = bool(false)]; tensor concat_36 = concat(axis = concat_36_axis_0, interleave = concat_36_interleave_0, values = (concat_36_values0_0, concat_36_values1_0, expand_dims_51, concat_36_values3_0))[name = string("concat_36")]; tensor k_cache2_internal_tensor_assign_6_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_6_stride_0"), val = tensor([1, 1, 1, 1])]; tensor k_cache2_internal_tensor_assign_6_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_6_begin_mask_0"), val = tensor([false, false, false, false])]; tensor k_cache2_internal_tensor_assign_6_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_6_end_mask_0"), val = tensor([false, true, false, true])]; tensor k_cache2_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_6_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor k_cache2_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_35, begin_mask = k_cache2_internal_tensor_assign_6_begin_mask_0, end = concat_36, end_mask = k_cache2_internal_tensor_assign_6_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_6_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_6_stride_0, update = linear_10_cast_fp16, x = coreml_update_state_24)[name = string("k_cache2_internal_tensor_assign_6_cast_fp16")]; write_state(data = k_cache2_internal_tensor_assign_6_cast_fp16, input = k_cache2)[name = string("coreml_update_state_26_write_state")]; tensor var_261_shape_cast_fp16 = shape(x = linear_11_cast_fp16)[name = string("op_261_shape_cast_fp16")]; int32 gather_11_axis_0 = const()[name = string("gather_11_axis_0"), val = int32(0)]; int32 gather_11_batch_dims_0 = const()[name = string("gather_11_batch_dims_0"), val = int32(0)]; bool gather_11_validate_indices_0 = const()[name = string("gather_11_validate_indices_0"), val = bool(false)]; string var_261_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_261_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; uint16 select_11_to_uint16 = const()[name = string("select_11_to_uint16"), val = uint16(1)]; tensor var_261_shape_cast_fp16_to_uint16 = cast(dtype = var_261_shape_cast_fp16_to_uint16_dtype_0, x = var_261_shape_cast_fp16)[name = string("cast_21")]; uint16 gather_11_cast_uint16 = gather(axis = gather_11_axis_0, batch_dims = gather_11_batch_dims_0, indices = select_11_to_uint16, validate_indices = gather_11_validate_indices_0, x = var_261_shape_cast_fp16_to_uint16)[name = string("gather_11_cast_uint16")]; string gather_11_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_11_cast_uint16_to_int32_dtype_0"), val = string("int32")]; tensor expand_dims_55_axes_0 = const()[name = string("expand_dims_55_axes_0"), val = tensor([0])]; int32 gather_11_cast_uint16_to_int32 = cast(dtype = gather_11_cast_uint16_to_int32_dtype_0, x = gather_11_cast_uint16)[name = string("cast_20")]; tensor expand_dims_55 = expand_dims(axes = expand_dims_55_axes_0, x = gather_11_cast_uint16_to_int32)[name = string("expand_dims_55")]; tensor concat_38 = const()[name = string("concat_38"), val = tensor([5, 0, 0, 0])]; tensor concat_39_values0_0 = const()[name = string("concat_39_values0_0"), val = tensor([0])]; tensor concat_39_values1_0 = const()[name = string("concat_39_values1_0"), val = tensor([0])]; tensor concat_39_values3_0 = const()[name = string("concat_39_values3_0"), val = tensor([0])]; int32 concat_39_axis_0 = const()[name = string("concat_39_axis_0"), val = int32(0)]; bool concat_39_interleave_0 = const()[name = string("concat_39_interleave_0"), val = bool(false)]; tensor concat_39 = concat(axis = concat_39_axis_0, interleave = concat_39_interleave_0, values = (concat_39_values0_0, concat_39_values1_0, expand_dims_55, concat_39_values3_0))[name = string("concat_39")]; tensor v_cache2_internal_tensor_assign_6_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_6_stride_0"), val = tensor([1, 1, 1, 1])]; tensor v_cache2_internal_tensor_assign_6_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_6_begin_mask_0"), val = tensor([false, false, false, false])]; tensor v_cache2_internal_tensor_assign_6_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_6_end_mask_0"), val = tensor([false, true, false, true])]; tensor v_cache2_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_6_squeeze_mask_0"), val = tensor([true, false, false, false])]; tensor v_cache2_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_38, begin_mask = v_cache2_internal_tensor_assign_6_begin_mask_0, end = concat_39, end_mask = v_cache2_internal_tensor_assign_6_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_6_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_6_stride_0, update = linear_11_cast_fp16, x = coreml_update_state_25)[name = string("v_cache2_internal_tensor_assign_6_cast_fp16")]; write_state(data = v_cache2_internal_tensor_assign_6_cast_fp16, input = v_cache2)[name = string("coreml_update_state_27_write_state")]; } -> (dummy); }