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program(1.3)
[buildInfo = dict<string, string>({{"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<ios18>(tensor<fp16, [1, ?, 768]> audio_data, state<tensor<fp16, [12, 1, 448, 768]>> k_cache1, state<tensor<fp16, [12, 1, 1500, 768]>> k_cache2, state<tensor<fp16, [12, 1, 448, 768]>> v_cache1, state<tensor<fp16, [12, 1, 1500, 768]>> v_cache2) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio_data", [1, 1, 768]}}), ("RangeDims", {{"audio_data", [[1, 1], [1, 1500], [768, 768]]}})))] {
tensor<fp16, [1, ?, 768]> dummy = identity(x = audio_data)[name = string("identity_0")];
tensor<fp16, [12, 1, 448, 768]> read_state_0 = read_state(input = k_cache1)[name = string("read_state_0")];
tensor<int32, [4]> concat_0 = const()[name = string("concat_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> concat_1 = const()[name = string("concat_1"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> k_cache1_internal_tensor_assign_1_stride_0 = const()[name = string("k_cache1_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache1_internal_tensor_assign_1_begin_mask_0 = const()[name = string("k_cache1_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache1_internal_tensor_assign_1_end_mask_0 = const()[name = string("k_cache1_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<bool, [4]> k_cache1_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("k_cache1_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<fp16, [12, 1, 448, 768]> const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = tensor<fp16, [12, 1, 448, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
tensor<fp16, [12, 1, 448, 768]> 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_26_write_state")];
tensor<fp16, [12, 1, 448, 768]> read_state_1 = read_state(input = v_cache1)[name = string("read_state_1")];
tensor<int32, [4]> concat_2 = const()[name = string("concat_2"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> concat_3 = const()[name = string("concat_3"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> v_cache1_internal_tensor_assign_1_stride_0 = const()[name = string("v_cache1_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache1_internal_tensor_assign_1_begin_mask_0 = const()[name = string("v_cache1_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache1_internal_tensor_assign_1_end_mask_0 = const()[name = string("v_cache1_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<bool, [4]> v_cache1_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("v_cache1_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<fp16, [12, 1, 448, 768]> 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_27_write_state")];
tensor<fp16, [12, 1, 1500, 768]> read_state_2 = read_state(input = k_cache2)[name = string("read_state_2")];
tensor<fp16, [12, 1, 1500, 768]> read_state_3 = read_state(input = v_cache2)[name = string("read_state_3")];
tensor<fp16, [768, 768]> var_91_to_fp16 = const()[name = string("op_91_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8257664)))];
tensor<fp16, [768]> linear_0_bias_0_to_fp16 = const()[name = string("linear_0_bias_0_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9437376)))];
tensor<fp16, [1, ?, 768]> linear_0_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_91_to_fp16, x = audio_data)[name = string("linear_0_cast_fp16")];
tensor<fp16, [768, 768]> var_95_to_fp16 = const()[name = string("op_95_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9438976)))];
tensor<fp16, [768]> var_96_to_fp16 = const()[name = string("op_96_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10618688)))];
tensor<fp16, [1, ?, 768]> linear_1_cast_fp16 = linear(bias = var_96_to_fp16, weight = var_95_to_fp16, x = audio_data)[name = string("linear_1_cast_fp16")];
tensor<int32, [3]> var_98_shape_cast_fp16 = shape(x = linear_0_cast_fp16)[name = string("op_98_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_98_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_98_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<int16, [3]> var_98_shape_cast_fp16_to_int16 = cast(dtype = var_98_shape_cast_fp16_to_int16_dtype_0, x = var_98_shape_cast_fp16)[name = string("cast_79")];
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_98_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<int32, [1]> expand_dims_11_axes_0 = const()[name = string("expand_dims_11_axes_0"), val = tensor<int32, [1]>([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_78")];
tensor<int32, [1]> expand_dims_11 = expand_dims(axes = expand_dims_11_axes_0, x = gather_0_cast_uint16_to_int32)[name = string("expand_dims_11")];
tensor<int32, [4]> concat_5 = const()[name = string("concat_5"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [1]> concat_6_values0_0 = const()[name = string("concat_6_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> k_cache2_internal_tensor_assign_1_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_1_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_1_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_28_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_28 = read_state(input = k_cache2)[name = string("coreml_update_state_28")];
tensor<int32, [3]> var_103_shape_cast_fp16 = shape(x = linear_1_cast_fp16)[name = string("op_103_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_103_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_103_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<uint16, [3]> var_103_shape_cast_fp16_to_uint16 = cast(dtype = var_103_shape_cast_fp16_to_uint16_dtype_0, x = var_103_shape_cast_fp16)[name = string("cast_77")];
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_103_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<int32, [1]> expand_dims_15_axes_0 = const()[name = string("expand_dims_15_axes_0"), val = tensor<int32, [1]>([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_76")];
tensor<int32, [1]> expand_dims_15 = expand_dims(axes = expand_dims_15_axes_0, x = gather_1_cast_uint16_to_int32)[name = string("expand_dims_15")];
tensor<int32, [4]> concat_8 = const()[name = string("concat_8"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [1]> concat_9_values0_0 = const()[name = string("concat_9_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_9_values1_0 = const()[name = string("concat_9_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_9_values3_0 = const()[name = string("concat_9_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> v_cache2_internal_tensor_assign_1_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_1_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_1_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_29_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_29 = read_state(input = v_cache2)[name = string("coreml_update_state_29")];
tensor<fp16, [768, 768]> var_125_to_fp16 = const()[name = string("op_125_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10620288)))];
tensor<fp16, [1, ?, 768]> linear_2_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_125_to_fp16, x = audio_data)[name = string("linear_2_cast_fp16")];
tensor<fp16, [768, 768]> var_129_to_fp16 = const()[name = string("op_129_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11800000)))];
tensor<fp16, [768]> var_130_to_fp16 = const()[name = string("op_130_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12979712)))];
tensor<fp16, [1, ?, 768]> linear_3_cast_fp16 = linear(bias = var_130_to_fp16, weight = var_129_to_fp16, x = audio_data)[name = string("linear_3_cast_fp16")];
tensor<int32, [3]> var_132_shape_cast_fp16 = shape(x = linear_2_cast_fp16)[name = string("op_132_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_132_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_132_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<uint16, [3]> var_132_shape_cast_fp16_to_uint16 = cast(dtype = var_132_shape_cast_fp16_to_uint16_dtype_0, x = var_132_shape_cast_fp16)[name = string("cast_75")];
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_132_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<int32, [1]> expand_dims_19_axes_0 = const()[name = string("expand_dims_19_axes_0"), val = tensor<int32, [1]>([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_74")];
tensor<int32, [1]> expand_dims_19 = expand_dims(axes = expand_dims_19_axes_0, x = gather_2_cast_uint16_to_int32)[name = string("expand_dims_19")];
tensor<int32, [4]> concat_11 = const()[name = string("concat_11"), val = tensor<int32, [4]>([1, 0, 0, 0])];
tensor<int32, [1]> concat_12_values0_0 = const()[name = string("concat_12_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_12_values1_0 = const()[name = string("concat_12_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_12_values3_0 = const()[name = string("concat_12_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> k_cache2_internal_tensor_assign_2_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_2_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_2_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_28)[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_30_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_30 = read_state(input = k_cache2)[name = string("coreml_update_state_30")];
tensor<int32, [3]> var_137_shape_cast_fp16 = shape(x = linear_3_cast_fp16)[name = string("op_137_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_137_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_137_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<uint16, [3]> var_137_shape_cast_fp16_to_uint16 = cast(dtype = var_137_shape_cast_fp16_to_uint16_dtype_0, x = var_137_shape_cast_fp16)[name = string("cast_73")];
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_137_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<int32, [1]> expand_dims_23_axes_0 = const()[name = string("expand_dims_23_axes_0"), val = tensor<int32, [1]>([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_72")];
tensor<int32, [1]> expand_dims_23 = expand_dims(axes = expand_dims_23_axes_0, x = gather_3_cast_uint16_to_int32)[name = string("expand_dims_23")];
tensor<int32, [4]> concat_14 = const()[name = string("concat_14"), val = tensor<int32, [4]>([1, 0, 0, 0])];
tensor<int32, [1]> concat_15_values0_0 = const()[name = string("concat_15_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_15_values1_0 = const()[name = string("concat_15_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_15_values3_0 = const()[name = string("concat_15_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> v_cache2_internal_tensor_assign_2_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_2_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_2_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_29)[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_31_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_31 = read_state(input = v_cache2)[name = string("coreml_update_state_31")];
tensor<fp16, [768, 768]> var_159_to_fp16 = const()[name = string("op_159_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12981312)))];
tensor<fp16, [1, ?, 768]> linear_4_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_159_to_fp16, x = audio_data)[name = string("linear_4_cast_fp16")];
tensor<fp16, [768, 768]> var_163_to_fp16 = const()[name = string("op_163_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14161024)))];
tensor<fp16, [768]> var_164_to_fp16 = const()[name = string("op_164_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15340736)))];
tensor<fp16, [1, ?, 768]> linear_5_cast_fp16 = linear(bias = var_164_to_fp16, weight = var_163_to_fp16, x = audio_data)[name = string("linear_5_cast_fp16")];
tensor<int32, [3]> var_166_shape_cast_fp16 = shape(x = linear_4_cast_fp16)[name = string("op_166_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_166_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_166_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<uint16, [3]> var_166_shape_cast_fp16_to_uint16 = cast(dtype = var_166_shape_cast_fp16_to_uint16_dtype_0, x = var_166_shape_cast_fp16)[name = string("cast_71")];
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_166_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<int32, [1]> expand_dims_27_axes_0 = const()[name = string("expand_dims_27_axes_0"), val = tensor<int32, [1]>([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_70")];
tensor<int32, [1]> expand_dims_27 = expand_dims(axes = expand_dims_27_axes_0, x = gather_4_cast_uint16_to_int32)[name = string("expand_dims_27")];
tensor<int32, [4]> concat_17 = const()[name = string("concat_17"), val = tensor<int32, [4]>([2, 0, 0, 0])];
tensor<int32, [1]> concat_18_values0_0 = const()[name = string("concat_18_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> k_cache2_internal_tensor_assign_3_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_3_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_3_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_30)[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_32_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_32 = read_state(input = k_cache2)[name = string("coreml_update_state_32")];
tensor<int32, [3]> var_171_shape_cast_fp16 = shape(x = linear_5_cast_fp16)[name = string("op_171_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_171_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_171_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<uint16, [3]> var_171_shape_cast_fp16_to_uint16 = cast(dtype = var_171_shape_cast_fp16_to_uint16_dtype_0, x = var_171_shape_cast_fp16)[name = string("cast_69")];
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_171_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<int32, [1]> expand_dims_31_axes_0 = const()[name = string("expand_dims_31_axes_0"), val = tensor<int32, [1]>([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_68")];
tensor<int32, [1]> expand_dims_31 = expand_dims(axes = expand_dims_31_axes_0, x = gather_5_cast_uint16_to_int32)[name = string("expand_dims_31")];
tensor<int32, [4]> concat_20 = const()[name = string("concat_20"), val = tensor<int32, [4]>([2, 0, 0, 0])];
tensor<int32, [1]> concat_21_values0_0 = const()[name = string("concat_21_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_21_values1_0 = const()[name = string("concat_21_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_21_values3_0 = const()[name = string("concat_21_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> v_cache2_internal_tensor_assign_3_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_3_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_3_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_31)[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_33_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_33 = read_state(input = v_cache2)[name = string("coreml_update_state_33")];
tensor<fp16, [768, 768]> var_193_to_fp16 = const()[name = string("op_193_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15342336)))];
tensor<fp16, [1, ?, 768]> linear_6_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_193_to_fp16, x = audio_data)[name = string("linear_6_cast_fp16")];
tensor<fp16, [768, 768]> var_197_to_fp16 = const()[name = string("op_197_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16522048)))];
tensor<fp16, [768]> var_198_to_fp16 = const()[name = string("op_198_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17701760)))];
tensor<fp16, [1, ?, 768]> linear_7_cast_fp16 = linear(bias = var_198_to_fp16, weight = var_197_to_fp16, x = audio_data)[name = string("linear_7_cast_fp16")];
tensor<int32, [3]> var_200_shape_cast_fp16 = shape(x = linear_6_cast_fp16)[name = string("op_200_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_200_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_200_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<uint16, [3]> var_200_shape_cast_fp16_to_uint16 = cast(dtype = var_200_shape_cast_fp16_to_uint16_dtype_0, x = var_200_shape_cast_fp16)[name = string("cast_67")];
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_200_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<int32, [1]> expand_dims_35_axes_0 = const()[name = string("expand_dims_35_axes_0"), val = tensor<int32, [1]>([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_66")];
tensor<int32, [1]> expand_dims_35 = expand_dims(axes = expand_dims_35_axes_0, x = gather_6_cast_uint16_to_int32)[name = string("expand_dims_35")];
tensor<int32, [4]> concat_23 = const()[name = string("concat_23"), val = tensor<int32, [4]>([3, 0, 0, 0])];
tensor<int32, [1]> concat_24_values0_0 = const()[name = string("concat_24_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_24_values1_0 = const()[name = string("concat_24_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_24_values3_0 = const()[name = string("concat_24_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> k_cache2_internal_tensor_assign_4_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_4_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_4_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_32)[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_34_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_34 = read_state(input = k_cache2)[name = string("coreml_update_state_34")];
tensor<int32, [3]> var_205_shape_cast_fp16 = shape(x = linear_7_cast_fp16)[name = string("op_205_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_205_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_205_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<uint16, [3]> var_205_shape_cast_fp16_to_uint16 = cast(dtype = var_205_shape_cast_fp16_to_uint16_dtype_0, x = var_205_shape_cast_fp16)[name = string("cast_65")];
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_205_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<int32, [1]> expand_dims_39_axes_0 = const()[name = string("expand_dims_39_axes_0"), val = tensor<int32, [1]>([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_64")];
tensor<int32, [1]> expand_dims_39 = expand_dims(axes = expand_dims_39_axes_0, x = gather_7_cast_uint16_to_int32)[name = string("expand_dims_39")];
tensor<int32, [4]> concat_26 = const()[name = string("concat_26"), val = tensor<int32, [4]>([3, 0, 0, 0])];
tensor<int32, [1]> concat_27_values0_0 = const()[name = string("concat_27_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_27_values1_0 = const()[name = string("concat_27_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_27_values3_0 = const()[name = string("concat_27_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> v_cache2_internal_tensor_assign_4_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_4_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_4_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_33)[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_35_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_35 = read_state(input = v_cache2)[name = string("coreml_update_state_35")];
tensor<fp16, [768, 768]> var_227_to_fp16 = const()[name = string("op_227_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17703360)))];
tensor<fp16, [1, ?, 768]> linear_8_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_227_to_fp16, x = audio_data)[name = string("linear_8_cast_fp16")];
tensor<fp16, [768, 768]> var_231_to_fp16 = const()[name = string("op_231_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18883072)))];
tensor<fp16, [768]> var_232_to_fp16 = const()[name = string("op_232_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20062784)))];
tensor<fp16, [1, ?, 768]> linear_9_cast_fp16 = linear(bias = var_232_to_fp16, weight = var_231_to_fp16, x = audio_data)[name = string("linear_9_cast_fp16")];
tensor<int32, [3]> var_234_shape_cast_fp16 = shape(x = linear_8_cast_fp16)[name = string("op_234_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_234_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_234_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<uint16, [3]> var_234_shape_cast_fp16_to_uint16 = cast(dtype = var_234_shape_cast_fp16_to_uint16_dtype_0, x = var_234_shape_cast_fp16)[name = string("cast_63")];
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_234_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<int32, [1]> expand_dims_43_axes_0 = const()[name = string("expand_dims_43_axes_0"), val = tensor<int32, [1]>([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_62")];
tensor<int32, [1]> expand_dims_43 = expand_dims(axes = expand_dims_43_axes_0, x = gather_8_cast_uint16_to_int32)[name = string("expand_dims_43")];
tensor<int32, [4]> concat_29 = const()[name = string("concat_29"), val = tensor<int32, [4]>([4, 0, 0, 0])];
tensor<int32, [1]> concat_30_values0_0 = const()[name = string("concat_30_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> k_cache2_internal_tensor_assign_5_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_5_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_5_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_34)[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_36_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_36 = read_state(input = k_cache2)[name = string("coreml_update_state_36")];
tensor<int32, [3]> var_239_shape_cast_fp16 = shape(x = linear_9_cast_fp16)[name = string("op_239_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_239_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_239_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<uint16, [3]> var_239_shape_cast_fp16_to_uint16 = cast(dtype = var_239_shape_cast_fp16_to_uint16_dtype_0, x = var_239_shape_cast_fp16)[name = string("cast_61")];
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_239_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<int32, [1]> expand_dims_47_axes_0 = const()[name = string("expand_dims_47_axes_0"), val = tensor<int32, [1]>([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_60")];
tensor<int32, [1]> expand_dims_47 = expand_dims(axes = expand_dims_47_axes_0, x = gather_9_cast_uint16_to_int32)[name = string("expand_dims_47")];
tensor<int32, [4]> concat_32 = const()[name = string("concat_32"), val = tensor<int32, [4]>([4, 0, 0, 0])];
tensor<int32, [1]> concat_33_values0_0 = const()[name = string("concat_33_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_33_values1_0 = const()[name = string("concat_33_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_33_values3_0 = const()[name = string("concat_33_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> v_cache2_internal_tensor_assign_5_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_5_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_5_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_35)[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_37_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_37 = read_state(input = v_cache2)[name = string("coreml_update_state_37")];
tensor<fp16, [768, 768]> var_261_to_fp16 = const()[name = string("op_261_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20064384)))];
tensor<fp16, [1, ?, 768]> linear_10_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_261_to_fp16, x = audio_data)[name = string("linear_10_cast_fp16")];
tensor<fp16, [768, 768]> var_265_to_fp16 = const()[name = string("op_265_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21244096)))];
tensor<fp16, [768]> var_266_to_fp16 = const()[name = string("op_266_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22423808)))];
tensor<fp16, [1, ?, 768]> linear_11_cast_fp16 = linear(bias = var_266_to_fp16, weight = var_265_to_fp16, x = audio_data)[name = string("linear_11_cast_fp16")];
tensor<int32, [3]> var_268_shape_cast_fp16 = shape(x = linear_10_cast_fp16)[name = string("op_268_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_268_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_268_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<uint16, [3]> var_268_shape_cast_fp16_to_uint16 = cast(dtype = var_268_shape_cast_fp16_to_uint16_dtype_0, x = var_268_shape_cast_fp16)[name = string("cast_59")];
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_268_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<int32, [1]> expand_dims_51_axes_0 = const()[name = string("expand_dims_51_axes_0"), val = tensor<int32, [1]>([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_58")];
tensor<int32, [1]> expand_dims_51 = expand_dims(axes = expand_dims_51_axes_0, x = gather_10_cast_uint16_to_int32)[name = string("expand_dims_51")];
tensor<int32, [4]> concat_35 = const()[name = string("concat_35"), val = tensor<int32, [4]>([5, 0, 0, 0])];
tensor<int32, [1]> concat_36_values0_0 = const()[name = string("concat_36_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_36_values1_0 = const()[name = string("concat_36_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_36_values3_0 = const()[name = string("concat_36_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> k_cache2_internal_tensor_assign_6_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_6_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_6_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_36)[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_38_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_38 = read_state(input = k_cache2)[name = string("coreml_update_state_38")];
tensor<int32, [3]> var_273_shape_cast_fp16 = shape(x = linear_11_cast_fp16)[name = string("op_273_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_273_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_273_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<uint16, [3]> var_273_shape_cast_fp16_to_uint16 = cast(dtype = var_273_shape_cast_fp16_to_uint16_dtype_0, x = var_273_shape_cast_fp16)[name = string("cast_57")];
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_273_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<int32, [1]> expand_dims_55_axes_0 = const()[name = string("expand_dims_55_axes_0"), val = tensor<int32, [1]>([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_56")];
tensor<int32, [1]> expand_dims_55 = expand_dims(axes = expand_dims_55_axes_0, x = gather_11_cast_uint16_to_int32)[name = string("expand_dims_55")];
tensor<int32, [4]> concat_38 = const()[name = string("concat_38"), val = tensor<int32, [4]>([5, 0, 0, 0])];
tensor<int32, [1]> concat_39_values0_0 = const()[name = string("concat_39_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_39_values1_0 = const()[name = string("concat_39_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_39_values3_0 = const()[name = string("concat_39_values3_0"), val = tensor<int32, [1]>([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<int32, [4]> 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<int32, [4]> v_cache2_internal_tensor_assign_6_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_6_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_6_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> 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_37)[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_39_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_39 = read_state(input = v_cache2)[name = string("coreml_update_state_39")];
tensor<fp16, [768, 768]> var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22425408)))];
tensor<fp16, [1, ?, 768]> linear_12_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_295_to_fp16, x = audio_data)[name = string("linear_12_cast_fp16")];
tensor<fp16, [768, 768]> var_299_to_fp16 = const()[name = string("op_299_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23605120)))];
tensor<fp16, [768]> var_300_to_fp16 = const()[name = string("op_300_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24784832)))];
tensor<fp16, [1, ?, 768]> linear_13_cast_fp16 = linear(bias = var_300_to_fp16, weight = var_299_to_fp16, x = audio_data)[name = string("linear_13_cast_fp16")];
tensor<int32, [3]> var_302_shape_cast_fp16 = shape(x = linear_12_cast_fp16)[name = string("op_302_shape_cast_fp16")];
int32 gather_12_axis_0 = const()[name = string("gather_12_axis_0"), val = int32(0)];
int32 gather_12_batch_dims_0 = const()[name = string("gather_12_batch_dims_0"), val = int32(0)];
bool gather_12_validate_indices_0 = const()[name = string("gather_12_validate_indices_0"), val = bool(false)];
string var_302_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_302_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_12_to_uint16 = const()[name = string("select_12_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_302_shape_cast_fp16_to_uint16 = cast(dtype = var_302_shape_cast_fp16_to_uint16_dtype_0, x = var_302_shape_cast_fp16)[name = string("cast_55")];
uint16 gather_12_cast_uint16 = gather(axis = gather_12_axis_0, batch_dims = gather_12_batch_dims_0, indices = select_12_to_uint16, validate_indices = gather_12_validate_indices_0, x = var_302_shape_cast_fp16_to_uint16)[name = string("gather_12_cast_uint16")];
string gather_12_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_12_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_59_axes_0 = const()[name = string("expand_dims_59_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_12_cast_uint16_to_int32 = cast(dtype = gather_12_cast_uint16_to_int32_dtype_0, x = gather_12_cast_uint16)[name = string("cast_54")];
tensor<int32, [1]> expand_dims_59 = expand_dims(axes = expand_dims_59_axes_0, x = gather_12_cast_uint16_to_int32)[name = string("expand_dims_59")];
tensor<int32, [4]> concat_41 = const()[name = string("concat_41"), val = tensor<int32, [4]>([6, 0, 0, 0])];
tensor<int32, [1]> concat_42_values0_0 = const()[name = string("concat_42_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_42_values1_0 = const()[name = string("concat_42_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_42_values3_0 = const()[name = string("concat_42_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (concat_42_values0_0, concat_42_values1_0, expand_dims_59, concat_42_values3_0))[name = string("concat_42")];
tensor<int32, [4]> k_cache2_internal_tensor_assign_7_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_7_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_7_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> k_cache2_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_41, begin_mask = k_cache2_internal_tensor_assign_7_begin_mask_0, end = concat_42, end_mask = k_cache2_internal_tensor_assign_7_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_7_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_7_stride_0, update = linear_12_cast_fp16, x = coreml_update_state_38)[name = string("k_cache2_internal_tensor_assign_7_cast_fp16")];
write_state(data = k_cache2_internal_tensor_assign_7_cast_fp16, input = k_cache2)[name = string("coreml_update_state_40_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_40 = read_state(input = k_cache2)[name = string("coreml_update_state_40")];
tensor<int32, [3]> var_307_shape_cast_fp16 = shape(x = linear_13_cast_fp16)[name = string("op_307_shape_cast_fp16")];
int32 gather_13_axis_0 = const()[name = string("gather_13_axis_0"), val = int32(0)];
int32 gather_13_batch_dims_0 = const()[name = string("gather_13_batch_dims_0"), val = int32(0)];
bool gather_13_validate_indices_0 = const()[name = string("gather_13_validate_indices_0"), val = bool(false)];
string var_307_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_307_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_13_to_uint16 = const()[name = string("select_13_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_307_shape_cast_fp16_to_uint16 = cast(dtype = var_307_shape_cast_fp16_to_uint16_dtype_0, x = var_307_shape_cast_fp16)[name = string("cast_53")];
uint16 gather_13_cast_uint16 = gather(axis = gather_13_axis_0, batch_dims = gather_13_batch_dims_0, indices = select_13_to_uint16, validate_indices = gather_13_validate_indices_0, x = var_307_shape_cast_fp16_to_uint16)[name = string("gather_13_cast_uint16")];
string gather_13_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_13_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_63_axes_0 = const()[name = string("expand_dims_63_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_13_cast_uint16_to_int32 = cast(dtype = gather_13_cast_uint16_to_int32_dtype_0, x = gather_13_cast_uint16)[name = string("cast_52")];
tensor<int32, [1]> expand_dims_63 = expand_dims(axes = expand_dims_63_axes_0, x = gather_13_cast_uint16_to_int32)[name = string("expand_dims_63")];
tensor<int32, [4]> concat_44 = const()[name = string("concat_44"), val = tensor<int32, [4]>([6, 0, 0, 0])];
tensor<int32, [1]> concat_45_values0_0 = const()[name = string("concat_45_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_45_values1_0 = const()[name = string("concat_45_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_45_values3_0 = const()[name = string("concat_45_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_45_axis_0 = const()[name = string("concat_45_axis_0"), val = int32(0)];
bool concat_45_interleave_0 = const()[name = string("concat_45_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_45 = concat(axis = concat_45_axis_0, interleave = concat_45_interleave_0, values = (concat_45_values0_0, concat_45_values1_0, expand_dims_63, concat_45_values3_0))[name = string("concat_45")];
tensor<int32, [4]> v_cache2_internal_tensor_assign_7_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_7_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_7_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> v_cache2_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_44, begin_mask = v_cache2_internal_tensor_assign_7_begin_mask_0, end = concat_45, end_mask = v_cache2_internal_tensor_assign_7_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_7_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_7_stride_0, update = linear_13_cast_fp16, x = coreml_update_state_39)[name = string("v_cache2_internal_tensor_assign_7_cast_fp16")];
write_state(data = v_cache2_internal_tensor_assign_7_cast_fp16, input = v_cache2)[name = string("coreml_update_state_41_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_41 = read_state(input = v_cache2)[name = string("coreml_update_state_41")];
tensor<fp16, [768, 768]> var_329_to_fp16 = const()[name = string("op_329_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24786432)))];
tensor<fp16, [1, ?, 768]> linear_14_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_329_to_fp16, x = audio_data)[name = string("linear_14_cast_fp16")];
tensor<fp16, [768, 768]> var_333_to_fp16 = const()[name = string("op_333_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25966144)))];
tensor<fp16, [768]> var_334_to_fp16 = const()[name = string("op_334_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27145856)))];
tensor<fp16, [1, ?, 768]> linear_15_cast_fp16 = linear(bias = var_334_to_fp16, weight = var_333_to_fp16, x = audio_data)[name = string("linear_15_cast_fp16")];
tensor<int32, [3]> var_336_shape_cast_fp16 = shape(x = linear_14_cast_fp16)[name = string("op_336_shape_cast_fp16")];
int32 gather_14_axis_0 = const()[name = string("gather_14_axis_0"), val = int32(0)];
int32 gather_14_batch_dims_0 = const()[name = string("gather_14_batch_dims_0"), val = int32(0)];
bool gather_14_validate_indices_0 = const()[name = string("gather_14_validate_indices_0"), val = bool(false)];
string var_336_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_336_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_14_to_uint16 = const()[name = string("select_14_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_336_shape_cast_fp16_to_uint16 = cast(dtype = var_336_shape_cast_fp16_to_uint16_dtype_0, x = var_336_shape_cast_fp16)[name = string("cast_51")];
uint16 gather_14_cast_uint16 = gather(axis = gather_14_axis_0, batch_dims = gather_14_batch_dims_0, indices = select_14_to_uint16, validate_indices = gather_14_validate_indices_0, x = var_336_shape_cast_fp16_to_uint16)[name = string("gather_14_cast_uint16")];
string gather_14_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_14_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_67_axes_0 = const()[name = string("expand_dims_67_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_14_cast_uint16_to_int32 = cast(dtype = gather_14_cast_uint16_to_int32_dtype_0, x = gather_14_cast_uint16)[name = string("cast_50")];
tensor<int32, [1]> expand_dims_67 = expand_dims(axes = expand_dims_67_axes_0, x = gather_14_cast_uint16_to_int32)[name = string("expand_dims_67")];
tensor<int32, [4]> concat_47 = const()[name = string("concat_47"), val = tensor<int32, [4]>([7, 0, 0, 0])];
tensor<int32, [1]> concat_48_values0_0 = const()[name = string("concat_48_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_48_values1_0 = const()[name = string("concat_48_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_48_values3_0 = const()[name = string("concat_48_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_48_axis_0 = const()[name = string("concat_48_axis_0"), val = int32(0)];
bool concat_48_interleave_0 = const()[name = string("concat_48_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_48 = concat(axis = concat_48_axis_0, interleave = concat_48_interleave_0, values = (concat_48_values0_0, concat_48_values1_0, expand_dims_67, concat_48_values3_0))[name = string("concat_48")];
tensor<int32, [4]> k_cache2_internal_tensor_assign_8_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_8_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_8_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> k_cache2_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_47, begin_mask = k_cache2_internal_tensor_assign_8_begin_mask_0, end = concat_48, end_mask = k_cache2_internal_tensor_assign_8_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_8_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_8_stride_0, update = linear_14_cast_fp16, x = coreml_update_state_40)[name = string("k_cache2_internal_tensor_assign_8_cast_fp16")];
write_state(data = k_cache2_internal_tensor_assign_8_cast_fp16, input = k_cache2)[name = string("coreml_update_state_42_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_42 = read_state(input = k_cache2)[name = string("coreml_update_state_42")];
tensor<int32, [3]> var_341_shape_cast_fp16 = shape(x = linear_15_cast_fp16)[name = string("op_341_shape_cast_fp16")];
int32 gather_15_axis_0 = const()[name = string("gather_15_axis_0"), val = int32(0)];
int32 gather_15_batch_dims_0 = const()[name = string("gather_15_batch_dims_0"), val = int32(0)];
bool gather_15_validate_indices_0 = const()[name = string("gather_15_validate_indices_0"), val = bool(false)];
string var_341_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_341_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_15_to_uint16 = const()[name = string("select_15_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_341_shape_cast_fp16_to_uint16 = cast(dtype = var_341_shape_cast_fp16_to_uint16_dtype_0, x = var_341_shape_cast_fp16)[name = string("cast_49")];
uint16 gather_15_cast_uint16 = gather(axis = gather_15_axis_0, batch_dims = gather_15_batch_dims_0, indices = select_15_to_uint16, validate_indices = gather_15_validate_indices_0, x = var_341_shape_cast_fp16_to_uint16)[name = string("gather_15_cast_uint16")];
string gather_15_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_15_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_71_axes_0 = const()[name = string("expand_dims_71_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_15_cast_uint16_to_int32 = cast(dtype = gather_15_cast_uint16_to_int32_dtype_0, x = gather_15_cast_uint16)[name = string("cast_48")];
tensor<int32, [1]> expand_dims_71 = expand_dims(axes = expand_dims_71_axes_0, x = gather_15_cast_uint16_to_int32)[name = string("expand_dims_71")];
tensor<int32, [4]> concat_50 = const()[name = string("concat_50"), val = tensor<int32, [4]>([7, 0, 0, 0])];
tensor<int32, [1]> concat_51_values0_0 = const()[name = string("concat_51_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_51_values1_0 = const()[name = string("concat_51_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_51_values3_0 = const()[name = string("concat_51_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_51_axis_0 = const()[name = string("concat_51_axis_0"), val = int32(0)];
bool concat_51_interleave_0 = const()[name = string("concat_51_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_51 = concat(axis = concat_51_axis_0, interleave = concat_51_interleave_0, values = (concat_51_values0_0, concat_51_values1_0, expand_dims_71, concat_51_values3_0))[name = string("concat_51")];
tensor<int32, [4]> v_cache2_internal_tensor_assign_8_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_8_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_8_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> v_cache2_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_50, begin_mask = v_cache2_internal_tensor_assign_8_begin_mask_0, end = concat_51, end_mask = v_cache2_internal_tensor_assign_8_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_8_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_8_stride_0, update = linear_15_cast_fp16, x = coreml_update_state_41)[name = string("v_cache2_internal_tensor_assign_8_cast_fp16")];
write_state(data = v_cache2_internal_tensor_assign_8_cast_fp16, input = v_cache2)[name = string("coreml_update_state_43_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_43 = read_state(input = v_cache2)[name = string("coreml_update_state_43")];
tensor<fp16, [768, 768]> var_363_to_fp16 = const()[name = string("op_363_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27147456)))];
tensor<fp16, [1, ?, 768]> linear_16_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_363_to_fp16, x = audio_data)[name = string("linear_16_cast_fp16")];
tensor<fp16, [768, 768]> var_367_to_fp16 = const()[name = string("op_367_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28327168)))];
tensor<fp16, [768]> var_368_to_fp16 = const()[name = string("op_368_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29506880)))];
tensor<fp16, [1, ?, 768]> linear_17_cast_fp16 = linear(bias = var_368_to_fp16, weight = var_367_to_fp16, x = audio_data)[name = string("linear_17_cast_fp16")];
tensor<int32, [3]> var_370_shape_cast_fp16 = shape(x = linear_16_cast_fp16)[name = string("op_370_shape_cast_fp16")];
int32 gather_16_axis_0 = const()[name = string("gather_16_axis_0"), val = int32(0)];
int32 gather_16_batch_dims_0 = const()[name = string("gather_16_batch_dims_0"), val = int32(0)];
bool gather_16_validate_indices_0 = const()[name = string("gather_16_validate_indices_0"), val = bool(false)];
string var_370_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_370_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_16_to_uint16 = const()[name = string("select_16_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_370_shape_cast_fp16_to_uint16 = cast(dtype = var_370_shape_cast_fp16_to_uint16_dtype_0, x = var_370_shape_cast_fp16)[name = string("cast_47")];
uint16 gather_16_cast_uint16 = gather(axis = gather_16_axis_0, batch_dims = gather_16_batch_dims_0, indices = select_16_to_uint16, validate_indices = gather_16_validate_indices_0, x = var_370_shape_cast_fp16_to_uint16)[name = string("gather_16_cast_uint16")];
string gather_16_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_16_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_75_axes_0 = const()[name = string("expand_dims_75_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_16_cast_uint16_to_int32 = cast(dtype = gather_16_cast_uint16_to_int32_dtype_0, x = gather_16_cast_uint16)[name = string("cast_46")];
tensor<int32, [1]> expand_dims_75 = expand_dims(axes = expand_dims_75_axes_0, x = gather_16_cast_uint16_to_int32)[name = string("expand_dims_75")];
tensor<int32, [4]> concat_53 = const()[name = string("concat_53"), val = tensor<int32, [4]>([8, 0, 0, 0])];
tensor<int32, [1]> concat_54_values0_0 = const()[name = string("concat_54_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_54_values1_0 = const()[name = string("concat_54_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_54_values3_0 = const()[name = string("concat_54_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (concat_54_values0_0, concat_54_values1_0, expand_dims_75, concat_54_values3_0))[name = string("concat_54")];
tensor<int32, [4]> k_cache2_internal_tensor_assign_9_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_9_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_9_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> k_cache2_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_53, begin_mask = k_cache2_internal_tensor_assign_9_begin_mask_0, end = concat_54, end_mask = k_cache2_internal_tensor_assign_9_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_9_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_9_stride_0, update = linear_16_cast_fp16, x = coreml_update_state_42)[name = string("k_cache2_internal_tensor_assign_9_cast_fp16")];
write_state(data = k_cache2_internal_tensor_assign_9_cast_fp16, input = k_cache2)[name = string("coreml_update_state_44_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_44 = read_state(input = k_cache2)[name = string("coreml_update_state_44")];
tensor<int32, [3]> var_375_shape_cast_fp16 = shape(x = linear_17_cast_fp16)[name = string("op_375_shape_cast_fp16")];
int32 gather_17_axis_0 = const()[name = string("gather_17_axis_0"), val = int32(0)];
int32 gather_17_batch_dims_0 = const()[name = string("gather_17_batch_dims_0"), val = int32(0)];
bool gather_17_validate_indices_0 = const()[name = string("gather_17_validate_indices_0"), val = bool(false)];
string var_375_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_375_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_17_to_uint16 = const()[name = string("select_17_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_375_shape_cast_fp16_to_uint16 = cast(dtype = var_375_shape_cast_fp16_to_uint16_dtype_0, x = var_375_shape_cast_fp16)[name = string("cast_45")];
uint16 gather_17_cast_uint16 = gather(axis = gather_17_axis_0, batch_dims = gather_17_batch_dims_0, indices = select_17_to_uint16, validate_indices = gather_17_validate_indices_0, x = var_375_shape_cast_fp16_to_uint16)[name = string("gather_17_cast_uint16")];
string gather_17_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_17_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_79_axes_0 = const()[name = string("expand_dims_79_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_17_cast_uint16_to_int32 = cast(dtype = gather_17_cast_uint16_to_int32_dtype_0, x = gather_17_cast_uint16)[name = string("cast_44")];
tensor<int32, [1]> expand_dims_79 = expand_dims(axes = expand_dims_79_axes_0, x = gather_17_cast_uint16_to_int32)[name = string("expand_dims_79")];
tensor<int32, [4]> concat_56 = const()[name = string("concat_56"), val = tensor<int32, [4]>([8, 0, 0, 0])];
tensor<int32, [1]> concat_57_values0_0 = const()[name = string("concat_57_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_57_values1_0 = const()[name = string("concat_57_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_57_values3_0 = const()[name = string("concat_57_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_57_axis_0 = const()[name = string("concat_57_axis_0"), val = int32(0)];
bool concat_57_interleave_0 = const()[name = string("concat_57_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_57 = concat(axis = concat_57_axis_0, interleave = concat_57_interleave_0, values = (concat_57_values0_0, concat_57_values1_0, expand_dims_79, concat_57_values3_0))[name = string("concat_57")];
tensor<int32, [4]> v_cache2_internal_tensor_assign_9_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_9_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_9_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> v_cache2_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_56, begin_mask = v_cache2_internal_tensor_assign_9_begin_mask_0, end = concat_57, end_mask = v_cache2_internal_tensor_assign_9_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_9_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_9_stride_0, update = linear_17_cast_fp16, x = coreml_update_state_43)[name = string("v_cache2_internal_tensor_assign_9_cast_fp16")];
write_state(data = v_cache2_internal_tensor_assign_9_cast_fp16, input = v_cache2)[name = string("coreml_update_state_45_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_45 = read_state(input = v_cache2)[name = string("coreml_update_state_45")];
tensor<fp16, [768, 768]> var_397_to_fp16 = const()[name = string("op_397_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29508480)))];
tensor<fp16, [1, ?, 768]> linear_18_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_397_to_fp16, x = audio_data)[name = string("linear_18_cast_fp16")];
tensor<fp16, [768, 768]> var_401_to_fp16 = const()[name = string("op_401_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30688192)))];
tensor<fp16, [768]> var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31867904)))];
tensor<fp16, [1, ?, 768]> linear_19_cast_fp16 = linear(bias = var_402_to_fp16, weight = var_401_to_fp16, x = audio_data)[name = string("linear_19_cast_fp16")];
tensor<int32, [3]> var_404_shape_cast_fp16 = shape(x = linear_18_cast_fp16)[name = string("op_404_shape_cast_fp16")];
int32 gather_18_axis_0 = const()[name = string("gather_18_axis_0"), val = int32(0)];
int32 gather_18_batch_dims_0 = const()[name = string("gather_18_batch_dims_0"), val = int32(0)];
bool gather_18_validate_indices_0 = const()[name = string("gather_18_validate_indices_0"), val = bool(false)];
string var_404_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_404_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_18_to_uint16 = const()[name = string("select_18_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_404_shape_cast_fp16_to_uint16 = cast(dtype = var_404_shape_cast_fp16_to_uint16_dtype_0, x = var_404_shape_cast_fp16)[name = string("cast_43")];
uint16 gather_18_cast_uint16 = gather(axis = gather_18_axis_0, batch_dims = gather_18_batch_dims_0, indices = select_18_to_uint16, validate_indices = gather_18_validate_indices_0, x = var_404_shape_cast_fp16_to_uint16)[name = string("gather_18_cast_uint16")];
string gather_18_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_18_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_83_axes_0 = const()[name = string("expand_dims_83_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_18_cast_uint16_to_int32 = cast(dtype = gather_18_cast_uint16_to_int32_dtype_0, x = gather_18_cast_uint16)[name = string("cast_42")];
tensor<int32, [1]> expand_dims_83 = expand_dims(axes = expand_dims_83_axes_0, x = gather_18_cast_uint16_to_int32)[name = string("expand_dims_83")];
tensor<int32, [4]> concat_59 = const()[name = string("concat_59"), val = tensor<int32, [4]>([9, 0, 0, 0])];
tensor<int32, [1]> concat_60_values0_0 = const()[name = string("concat_60_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_60_values1_0 = const()[name = string("concat_60_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_60_values3_0 = const()[name = string("concat_60_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_60_axis_0 = const()[name = string("concat_60_axis_0"), val = int32(0)];
bool concat_60_interleave_0 = const()[name = string("concat_60_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_60 = concat(axis = concat_60_axis_0, interleave = concat_60_interleave_0, values = (concat_60_values0_0, concat_60_values1_0, expand_dims_83, concat_60_values3_0))[name = string("concat_60")];
tensor<int32, [4]> k_cache2_internal_tensor_assign_10_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_10_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_10_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> k_cache2_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_59, begin_mask = k_cache2_internal_tensor_assign_10_begin_mask_0, end = concat_60, end_mask = k_cache2_internal_tensor_assign_10_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_10_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_10_stride_0, update = linear_18_cast_fp16, x = coreml_update_state_44)[name = string("k_cache2_internal_tensor_assign_10_cast_fp16")];
write_state(data = k_cache2_internal_tensor_assign_10_cast_fp16, input = k_cache2)[name = string("coreml_update_state_46_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_46 = read_state(input = k_cache2)[name = string("coreml_update_state_46")];
tensor<int32, [3]> var_409_shape_cast_fp16 = shape(x = linear_19_cast_fp16)[name = string("op_409_shape_cast_fp16")];
int32 gather_19_axis_0 = const()[name = string("gather_19_axis_0"), val = int32(0)];
int32 gather_19_batch_dims_0 = const()[name = string("gather_19_batch_dims_0"), val = int32(0)];
bool gather_19_validate_indices_0 = const()[name = string("gather_19_validate_indices_0"), val = bool(false)];
string var_409_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_409_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_19_to_uint16 = const()[name = string("select_19_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_409_shape_cast_fp16_to_uint16 = cast(dtype = var_409_shape_cast_fp16_to_uint16_dtype_0, x = var_409_shape_cast_fp16)[name = string("cast_41")];
uint16 gather_19_cast_uint16 = gather(axis = gather_19_axis_0, batch_dims = gather_19_batch_dims_0, indices = select_19_to_uint16, validate_indices = gather_19_validate_indices_0, x = var_409_shape_cast_fp16_to_uint16)[name = string("gather_19_cast_uint16")];
string gather_19_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_19_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_87_axes_0 = const()[name = string("expand_dims_87_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_19_cast_uint16_to_int32 = cast(dtype = gather_19_cast_uint16_to_int32_dtype_0, x = gather_19_cast_uint16)[name = string("cast_40")];
tensor<int32, [1]> expand_dims_87 = expand_dims(axes = expand_dims_87_axes_0, x = gather_19_cast_uint16_to_int32)[name = string("expand_dims_87")];
tensor<int32, [4]> concat_62 = const()[name = string("concat_62"), val = tensor<int32, [4]>([9, 0, 0, 0])];
tensor<int32, [1]> concat_63_values0_0 = const()[name = string("concat_63_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_63_values1_0 = const()[name = string("concat_63_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_63_values3_0 = const()[name = string("concat_63_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_63_axis_0 = const()[name = string("concat_63_axis_0"), val = int32(0)];
bool concat_63_interleave_0 = const()[name = string("concat_63_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_63 = concat(axis = concat_63_axis_0, interleave = concat_63_interleave_0, values = (concat_63_values0_0, concat_63_values1_0, expand_dims_87, concat_63_values3_0))[name = string("concat_63")];
tensor<int32, [4]> v_cache2_internal_tensor_assign_10_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_10_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_10_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> v_cache2_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_62, begin_mask = v_cache2_internal_tensor_assign_10_begin_mask_0, end = concat_63, end_mask = v_cache2_internal_tensor_assign_10_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_10_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_10_stride_0, update = linear_19_cast_fp16, x = coreml_update_state_45)[name = string("v_cache2_internal_tensor_assign_10_cast_fp16")];
write_state(data = v_cache2_internal_tensor_assign_10_cast_fp16, input = v_cache2)[name = string("coreml_update_state_47_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_47 = read_state(input = v_cache2)[name = string("coreml_update_state_47")];
tensor<fp16, [768, 768]> var_431_to_fp16 = const()[name = string("op_431_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31869504)))];
tensor<fp16, [1, ?, 768]> linear_20_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_431_to_fp16, x = audio_data)[name = string("linear_20_cast_fp16")];
tensor<fp16, [768, 768]> var_435_to_fp16 = const()[name = string("op_435_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33049216)))];
tensor<fp16, [768]> var_436_to_fp16 = const()[name = string("op_436_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34228928)))];
tensor<fp16, [1, ?, 768]> linear_21_cast_fp16 = linear(bias = var_436_to_fp16, weight = var_435_to_fp16, x = audio_data)[name = string("linear_21_cast_fp16")];
tensor<int32, [3]> var_438_shape_cast_fp16 = shape(x = linear_20_cast_fp16)[name = string("op_438_shape_cast_fp16")];
int32 gather_20_axis_0 = const()[name = string("gather_20_axis_0"), val = int32(0)];
int32 gather_20_batch_dims_0 = const()[name = string("gather_20_batch_dims_0"), val = int32(0)];
bool gather_20_validate_indices_0 = const()[name = string("gather_20_validate_indices_0"), val = bool(false)];
string var_438_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_438_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_20_to_uint16 = const()[name = string("select_20_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_438_shape_cast_fp16_to_uint16 = cast(dtype = var_438_shape_cast_fp16_to_uint16_dtype_0, x = var_438_shape_cast_fp16)[name = string("cast_39")];
uint16 gather_20_cast_uint16 = gather(axis = gather_20_axis_0, batch_dims = gather_20_batch_dims_0, indices = select_20_to_uint16, validate_indices = gather_20_validate_indices_0, x = var_438_shape_cast_fp16_to_uint16)[name = string("gather_20_cast_uint16")];
string gather_20_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_20_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_91_axes_0 = const()[name = string("expand_dims_91_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_20_cast_uint16_to_int32 = cast(dtype = gather_20_cast_uint16_to_int32_dtype_0, x = gather_20_cast_uint16)[name = string("cast_38")];
tensor<int32, [1]> expand_dims_91 = expand_dims(axes = expand_dims_91_axes_0, x = gather_20_cast_uint16_to_int32)[name = string("expand_dims_91")];
tensor<int32, [4]> concat_65 = const()[name = string("concat_65"), val = tensor<int32, [4]>([10, 0, 0, 0])];
tensor<int32, [1]> concat_66_values0_0 = const()[name = string("concat_66_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_66_values1_0 = const()[name = string("concat_66_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_66_values3_0 = const()[name = string("concat_66_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_66_axis_0 = const()[name = string("concat_66_axis_0"), val = int32(0)];
bool concat_66_interleave_0 = const()[name = string("concat_66_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (concat_66_values0_0, concat_66_values1_0, expand_dims_91, concat_66_values3_0))[name = string("concat_66")];
tensor<int32, [4]> k_cache2_internal_tensor_assign_11_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_11_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_11_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> k_cache2_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_65, begin_mask = k_cache2_internal_tensor_assign_11_begin_mask_0, end = concat_66, end_mask = k_cache2_internal_tensor_assign_11_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_11_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_11_stride_0, update = linear_20_cast_fp16, x = coreml_update_state_46)[name = string("k_cache2_internal_tensor_assign_11_cast_fp16")];
write_state(data = k_cache2_internal_tensor_assign_11_cast_fp16, input = k_cache2)[name = string("coreml_update_state_48_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_48 = read_state(input = k_cache2)[name = string("coreml_update_state_48")];
tensor<int32, [3]> var_443_shape_cast_fp16 = shape(x = linear_21_cast_fp16)[name = string("op_443_shape_cast_fp16")];
int32 gather_21_axis_0 = const()[name = string("gather_21_axis_0"), val = int32(0)];
int32 gather_21_batch_dims_0 = const()[name = string("gather_21_batch_dims_0"), val = int32(0)];
bool gather_21_validate_indices_0 = const()[name = string("gather_21_validate_indices_0"), val = bool(false)];
string var_443_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_443_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_21_to_uint16 = const()[name = string("select_21_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_443_shape_cast_fp16_to_uint16 = cast(dtype = var_443_shape_cast_fp16_to_uint16_dtype_0, x = var_443_shape_cast_fp16)[name = string("cast_37")];
uint16 gather_21_cast_uint16 = gather(axis = gather_21_axis_0, batch_dims = gather_21_batch_dims_0, indices = select_21_to_uint16, validate_indices = gather_21_validate_indices_0, x = var_443_shape_cast_fp16_to_uint16)[name = string("gather_21_cast_uint16")];
string gather_21_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_21_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_95_axes_0 = const()[name = string("expand_dims_95_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_21_cast_uint16_to_int32 = cast(dtype = gather_21_cast_uint16_to_int32_dtype_0, x = gather_21_cast_uint16)[name = string("cast_36")];
tensor<int32, [1]> expand_dims_95 = expand_dims(axes = expand_dims_95_axes_0, x = gather_21_cast_uint16_to_int32)[name = string("expand_dims_95")];
tensor<int32, [4]> concat_68 = const()[name = string("concat_68"), val = tensor<int32, [4]>([10, 0, 0, 0])];
tensor<int32, [1]> concat_69_values0_0 = const()[name = string("concat_69_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_69_values1_0 = const()[name = string("concat_69_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_69_values3_0 = const()[name = string("concat_69_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_69_axis_0 = const()[name = string("concat_69_axis_0"), val = int32(0)];
bool concat_69_interleave_0 = const()[name = string("concat_69_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_69 = concat(axis = concat_69_axis_0, interleave = concat_69_interleave_0, values = (concat_69_values0_0, concat_69_values1_0, expand_dims_95, concat_69_values3_0))[name = string("concat_69")];
tensor<int32, [4]> v_cache2_internal_tensor_assign_11_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_11_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_11_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> v_cache2_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_68, begin_mask = v_cache2_internal_tensor_assign_11_begin_mask_0, end = concat_69, end_mask = v_cache2_internal_tensor_assign_11_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_11_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_11_stride_0, update = linear_21_cast_fp16, x = coreml_update_state_47)[name = string("v_cache2_internal_tensor_assign_11_cast_fp16")];
write_state(data = v_cache2_internal_tensor_assign_11_cast_fp16, input = v_cache2)[name = string("coreml_update_state_49_write_state")];
tensor<fp16, [12, 1, 1500, 768]> coreml_update_state_49 = read_state(input = v_cache2)[name = string("coreml_update_state_49")];
tensor<fp16, [768, 768]> var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34230528)))];
tensor<fp16, [1, ?, 768]> linear_22_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = var_465_to_fp16, x = audio_data)[name = string("linear_22_cast_fp16")];
tensor<fp16, [768, 768]> var_469_to_fp16 = const()[name = string("op_469_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35410240)))];
tensor<fp16, [768]> var_470_to_fp16 = const()[name = string("op_470_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36589952)))];
tensor<fp16, [1, ?, 768]> linear_23_cast_fp16 = linear(bias = var_470_to_fp16, weight = var_469_to_fp16, x = audio_data)[name = string("linear_23_cast_fp16")];
tensor<int32, [3]> var_472_shape_cast_fp16 = shape(x = linear_22_cast_fp16)[name = string("op_472_shape_cast_fp16")];
int32 gather_22_axis_0 = const()[name = string("gather_22_axis_0"), val = int32(0)];
int32 gather_22_batch_dims_0 = const()[name = string("gather_22_batch_dims_0"), val = int32(0)];
bool gather_22_validate_indices_0 = const()[name = string("gather_22_validate_indices_0"), val = bool(false)];
string var_472_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_472_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_22_to_uint16 = const()[name = string("select_22_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_472_shape_cast_fp16_to_uint16 = cast(dtype = var_472_shape_cast_fp16_to_uint16_dtype_0, x = var_472_shape_cast_fp16)[name = string("cast_35")];
uint16 gather_22_cast_uint16 = gather(axis = gather_22_axis_0, batch_dims = gather_22_batch_dims_0, indices = select_22_to_uint16, validate_indices = gather_22_validate_indices_0, x = var_472_shape_cast_fp16_to_uint16)[name = string("gather_22_cast_uint16")];
string gather_22_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_22_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_99_axes_0 = const()[name = string("expand_dims_99_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_22_cast_uint16_to_int32 = cast(dtype = gather_22_cast_uint16_to_int32_dtype_0, x = gather_22_cast_uint16)[name = string("cast_34")];
tensor<int32, [1]> expand_dims_99 = expand_dims(axes = expand_dims_99_axes_0, x = gather_22_cast_uint16_to_int32)[name = string("expand_dims_99")];
tensor<int32, [4]> concat_71 = const()[name = string("concat_71"), val = tensor<int32, [4]>([11, 0, 0, 0])];
tensor<int32, [1]> concat_72_values0_0 = const()[name = string("concat_72_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_72_values1_0 = const()[name = string("concat_72_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_72_values3_0 = const()[name = string("concat_72_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_72_axis_0 = const()[name = string("concat_72_axis_0"), val = int32(0)];
bool concat_72_interleave_0 = const()[name = string("concat_72_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_72 = concat(axis = concat_72_axis_0, interleave = concat_72_interleave_0, values = (concat_72_values0_0, concat_72_values1_0, expand_dims_99, concat_72_values3_0))[name = string("concat_72")];
tensor<int32, [4]> k_cache2_internal_tensor_assign_12_stride_0 = const()[name = string("k_cache2_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_12_begin_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_12_end_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> k_cache2_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("k_cache2_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> k_cache2_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_71, begin_mask = k_cache2_internal_tensor_assign_12_begin_mask_0, end = concat_72, end_mask = k_cache2_internal_tensor_assign_12_end_mask_0, squeeze_mask = k_cache2_internal_tensor_assign_12_squeeze_mask_0, stride = k_cache2_internal_tensor_assign_12_stride_0, update = linear_22_cast_fp16, x = coreml_update_state_48)[name = string("k_cache2_internal_tensor_assign_12_cast_fp16")];
write_state(data = k_cache2_internal_tensor_assign_12_cast_fp16, input = k_cache2)[name = string("coreml_update_state_50_write_state")];
tensor<int32, [3]> var_477_shape_cast_fp16 = shape(x = linear_23_cast_fp16)[name = string("op_477_shape_cast_fp16")];
int32 gather_23_axis_0 = const()[name = string("gather_23_axis_0"), val = int32(0)];
int32 gather_23_batch_dims_0 = const()[name = string("gather_23_batch_dims_0"), val = int32(0)];
bool gather_23_validate_indices_0 = const()[name = string("gather_23_validate_indices_0"), val = bool(false)];
string var_477_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_477_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")];
uint16 select_23_to_uint16 = const()[name = string("select_23_to_uint16"), val = uint16(1)];
tensor<uint16, [3]> var_477_shape_cast_fp16_to_uint16 = cast(dtype = var_477_shape_cast_fp16_to_uint16_dtype_0, x = var_477_shape_cast_fp16)[name = string("cast_33")];
uint16 gather_23_cast_uint16 = gather(axis = gather_23_axis_0, batch_dims = gather_23_batch_dims_0, indices = select_23_to_uint16, validate_indices = gather_23_validate_indices_0, x = var_477_shape_cast_fp16_to_uint16)[name = string("gather_23_cast_uint16")];
string gather_23_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_23_cast_uint16_to_int32_dtype_0"), val = string("int32")];
tensor<int32, [1]> expand_dims_103_axes_0 = const()[name = string("expand_dims_103_axes_0"), val = tensor<int32, [1]>([0])];
int32 gather_23_cast_uint16_to_int32 = cast(dtype = gather_23_cast_uint16_to_int32_dtype_0, x = gather_23_cast_uint16)[name = string("cast_32")];
tensor<int32, [1]> expand_dims_103 = expand_dims(axes = expand_dims_103_axes_0, x = gather_23_cast_uint16_to_int32)[name = string("expand_dims_103")];
tensor<int32, [4]> concat_74 = const()[name = string("concat_74"), val = tensor<int32, [4]>([11, 0, 0, 0])];
tensor<int32, [1]> concat_75_values0_0 = const()[name = string("concat_75_values0_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_75_values1_0 = const()[name = string("concat_75_values1_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1]> concat_75_values3_0 = const()[name = string("concat_75_values3_0"), val = tensor<int32, [1]>([0])];
int32 concat_75_axis_0 = const()[name = string("concat_75_axis_0"), val = int32(0)];
bool concat_75_interleave_0 = const()[name = string("concat_75_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_75 = concat(axis = concat_75_axis_0, interleave = concat_75_interleave_0, values = (concat_75_values0_0, concat_75_values1_0, expand_dims_103, concat_75_values3_0))[name = string("concat_75")];
tensor<int32, [4]> v_cache2_internal_tensor_assign_12_stride_0 = const()[name = string("v_cache2_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_12_begin_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_12_end_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
tensor<bool, [4]> v_cache2_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("v_cache2_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([true, false, false, false])];
tensor<fp16, [12, 1, 1500, 768]> v_cache2_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_74, begin_mask = v_cache2_internal_tensor_assign_12_begin_mask_0, end = concat_75, end_mask = v_cache2_internal_tensor_assign_12_end_mask_0, squeeze_mask = v_cache2_internal_tensor_assign_12_squeeze_mask_0, stride = v_cache2_internal_tensor_assign_12_stride_0, update = linear_23_cast_fp16, x = coreml_update_state_49)[name = string("v_cache2_internal_tensor_assign_12_cast_fp16")];
write_state(data = v_cache2_internal_tensor_assign_12_cast_fp16, input = v_cache2)[name = string("coreml_update_state_51_write_state")];
} -> (dummy);
} |