program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-tensorflow", "2.13.0"}, {"coremltools-version", "8.2"}, {"mldb_token", "mldb-wzxy8wby59"}})]
{
    func main<ios15>(tensor<fp32, [1, 2257]> x) {
            tensor<int32, []> model_lstm_PartitionedCall_time = const()[name = tensor<string, []>("model_lstm_PartitionedCall_time"), val = tensor<int32, []>(0)];
            tensor<int32, []> model_lstm_PartitionedCall_TensorArrayV2_1_num_elements = const()[name = tensor<string, []>("model_lstm_PartitionedCall_TensorArrayV2_1_num_elements"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> model_tf_split_1_Const = const()[name = tensor<string, []>("model_tf_split_1_Const"), val = tensor<int32, [3]>([256, 2000, 1])];
            tensor<int32, []> model_tf_split_1_split_split_dim = const()[name = tensor<string, []>("model_tf_split_1_split_split_dim"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> model_lstm_PartitionedCall_transpose_perm = const()[name = tensor<string, []>("model_lstm_PartitionedCall_transpose_perm"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [2]> model_flatten_1_Const = const()[name = tensor<string, []>("model_flatten_1_Const"), val = tensor<int32, [2]>([-1, 256])];
            tensor<int32, []> model_concatenate_concat_axis = const()[name = tensor<string, []>("model_concatenate_concat_axis"), val = tensor<int32, []>(1)];
            tensor<fp32, [64, 256]> Func_model_lstm_PartitionedCall_input__3 = const()[name = tensor<string, []>("Func_model_lstm_PartitionedCall_input__3"), val = tensor<fp32, [64, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [64, 256]> Func_model_lstm_PartitionedCall_input__4 = const()[name = tensor<string, []>("Func_model_lstm_PartitionedCall_input__4"), val = tensor<fp32, [64, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65664)))];
            tensor<fp32, [256]> Func_model_lstm_PartitionedCall_input__5 = const()[name = tensor<string, []>("Func_model_lstm_PartitionedCall_input__5"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131264)))];
            tensor<string, []> model_lstm_PartitionedCall_TensorArrayV2_1_dtype_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_TensorArrayV2_1_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<bool, []> model_lstm_PartitionedCall_TensorArrayV2_1_dynamic_length_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_TensorArrayV2_1_dynamic_length_0"), val = tensor<bool, []>(false)];
            tensor<int32, []> model_lstm_PartitionedCall_TensorArrayV2_1_elem_shape0_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_TensorArrayV2_1_elem_shape0_0"), val = tensor<int32, []>(1)];
            tensor<int32, []> model_lstm_PartitionedCall_TensorArrayV2_1_elem_shape1_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_TensorArrayV2_1_elem_shape1_0"), val = tensor<int32, []>(64)];
            list<tensor<fp32, [1, 64]>, 1> model_lstm_PartitionedCall_TensorArrayV2_1 = make_list(dtype = model_lstm_PartitionedCall_TensorArrayV2_1_dtype_0, dynamic_length = model_lstm_PartitionedCall_TensorArrayV2_1_dynamic_length_0, elem_shape = (model_lstm_PartitionedCall_TensorArrayV2_1_elem_shape0_0, model_lstm_PartitionedCall_TensorArrayV2_1_elem_shape1_0), init_length = model_lstm_PartitionedCall_TensorArrayV2_1_num_elements)[name = tensor<string, []>("model_lstm_PartitionedCall_TensorArrayV2_1")];
            tensor<int32, []> model_tf_split_1_split_num_splits_0 = const()[name = tensor<string, []>("model_tf_split_1_split_num_splits_0"), val = tensor<int32, []>(3)];
            tensor<fp32, [1, 256]> model_tf_split_1_split_0, tensor<fp32, [1, 2000]> model_tf_split_1_split_1, tensor<fp32, [1, 1]> model_tf_split_1_split_2 = split(axis = model_tf_split_1_split_split_dim, num_splits = model_tf_split_1_split_num_splits_0, split_sizes = model_tf_split_1_Const, x = x)[name = tensor<string, []>("model_tf_split_1_split")];
            tensor<string, []> model_embedding_Cast_dtype_0 = const()[name = tensor<string, []>("model_embedding_Cast_dtype_0"), val = tensor<string, []>("int32")];
            tensor<string, []> model_embedding_1_Cast_dtype_0 = const()[name = tensor<string, []>("model_embedding_1_Cast_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, []> model_embedding_embedding_lookup_axis_0 = const()[name = tensor<string, []>("model_embedding_embedding_lookup_axis_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [10000, 64]> model_embedding_embedding_lookup_23646_to_fp16 = const()[name = tensor<string, []>("model_embedding_embedding_lookup_23646_to_fp16"), val = tensor<fp16, [10000, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132352)))];
            tensor<int32, [1, 256]> model_embedding_Cast = cast(dtype = model_embedding_Cast_dtype_0, x = model_tf_split_1_split_0)[name = tensor<string, []>("cast_11")];
            tensor<fp16, [1, 256, 64]> model_embedding_embedding_lookup_cast_fp16 = gather(axis = model_embedding_embedding_lookup_axis_0, indices = model_embedding_Cast, x = model_embedding_embedding_lookup_23646_to_fp16)[name = tensor<string, []>("model_embedding_embedding_lookup_cast_fp16")];
            tensor<string, []> model_embedding_embedding_lookup_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("model_embedding_embedding_lookup_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> model_embedding_1_embedding_lookup_axis_0 = const()[name = tensor<string, []>("model_embedding_1_embedding_lookup_axis_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [621, 256]> model_embedding_1_embedding_lookup_23651_to_fp16 = const()[name = tensor<string, []>("model_embedding_1_embedding_lookup_23651_to_fp16"), val = tensor<fp16, [621, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412416)))];
            tensor<int32, [1, 1]> model_embedding_1_Cast = cast(dtype = model_embedding_1_Cast_dtype_0, x = model_tf_split_1_split_2)[name = tensor<string, []>("cast_10")];
            tensor<fp16, [1, 1, 256]> model_embedding_1_embedding_lookup_cast_fp16 = gather(axis = model_embedding_1_embedding_lookup_axis_0, indices = model_embedding_1_Cast, x = model_embedding_1_embedding_lookup_23651_to_fp16)[name = tensor<string, []>("model_embedding_1_embedding_lookup_cast_fp16")];
            tensor<string, []> model_embedding_1_embedding_lookup_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("model_embedding_1_embedding_lookup_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 1, 256]> model_embedding_1_embedding_lookup_cast_fp16_to_fp32 = cast(dtype = model_embedding_1_embedding_lookup_cast_fp16_to_fp32_dtype_0, x = model_embedding_1_embedding_lookup_cast_fp16)[name = tensor<string, []>("cast_8")];
            tensor<fp32, [1, 256]> model_flatten_1_Reshape = reshape(shape = model_flatten_1_Const, x = model_embedding_1_embedding_lookup_cast_fp16_to_fp32)[name = tensor<string, []>("model_flatten_1_Reshape")];
            tensor<string, []> model_flatten_1_Reshape_to_fp16_dtype_0 = const()[name = tensor<string, []>("model_flatten_1_Reshape_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [256, 256]> transpose_0_to_fp16 = const()[name = tensor<string, []>("transpose_0_to_fp16"), val = tensor<fp16, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1730432)))];
            tensor<fp16, [256]> model_dense_1_BiasAdd_bias_0_to_fp16 = const()[name = tensor<string, []>("model_dense_1_BiasAdd_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1861568)))];
            tensor<fp16, [1, 256]> model_flatten_1_Reshape_to_fp16 = cast(dtype = model_flatten_1_Reshape_to_fp16_dtype_0, x = model_flatten_1_Reshape)[name = tensor<string, []>("cast_7")];
            tensor<fp16, [1, 256]> model_dense_1_BiasAdd_cast_fp16 = linear(bias = model_dense_1_BiasAdd_bias_0_to_fp16, weight = transpose_0_to_fp16, x = model_flatten_1_Reshape_to_fp16)[name = tensor<string, []>("model_dense_1_BiasAdd_cast_fp16")];
            tensor<string, []> model_dense_1_BiasAdd_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("model_dense_1_BiasAdd_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 64]> model_lstm_zeros = const()[name = tensor<string, []>("model_lstm_zeros"), val = tensor<fp32, [1, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1862144)))];
            tensor<fp32, [1, 64]> model_lstm_zeros_1 = const()[name = tensor<string, []>("model_lstm_zeros_1"), val = tensor<fp32, [1, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1862464)))];
            tensor<fp32, [1, 256]> model_dense_1_BiasAdd_cast_fp16_to_fp32 = cast(dtype = model_dense_1_BiasAdd_cast_fp16_to_fp32_dtype_0, x = model_dense_1_BiasAdd_cast_fp16)[name = tensor<string, []>("cast_6")];
            tensor<fp32, [1, 256]> model_dense_1_Relu = relu(x = model_dense_1_BiasAdd_cast_fp16_to_fp32)[name = tensor<string, []>("model_dense_1_Relu")];
            tensor<int32, []> slice_by_index_0 = const()[name = tensor<string, []>("slice_by_index_0"), val = tensor<int32, []>(256)];
            tensor<string, []> tf_make_list_0_dtype_0 = const()[name = tensor<string, []>("tf_make_list_0_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<bool, []> tf_make_list_0_dynamic_length_0 = const()[name = tensor<string, []>("tf_make_list_0_dynamic_length_0"), val = tensor<bool, []>(true)];
            tensor<int32, []> tf_make_list_0_elem_shape0_0 = const()[name = tensor<string, []>("tf_make_list_0_elem_shape0_0"), val = tensor<int32, []>(1)];
            tensor<int32, []> tf_make_list_0_elem_shape1_0 = const()[name = tensor<string, []>("tf_make_list_0_elem_shape1_0"), val = tensor<int32, []>(64)];
            list<tensor<fp32, [1, 64]>, 256> tf_make_list_0 = make_list(dtype = tf_make_list_0_dtype_0, dynamic_length = tf_make_list_0_dynamic_length_0, elem_shape = (tf_make_list_0_elem_shape0_0, tf_make_list_0_elem_shape1_0), init_length = slice_by_index_0)[name = tensor<string, []>("tf_make_list_0")];
            tensor<int32, [256]> range_1d_0 = const()[name = tensor<string, []>("range_1d_0"), val = tensor<int32, [256]>([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255])];
            tensor<fp32, [1, 256, 64]> model_embedding_embedding_lookup_cast_fp16_to_fp32 = cast(dtype = model_embedding_embedding_lookup_cast_fp16_to_fp32_dtype_0, x = model_embedding_embedding_lookup_cast_fp16)[name = tensor<string, []>("cast_9")];
            tensor<fp32, [256, 1, 64]> model_lstm_PartitionedCall_transpose = transpose(perm = model_lstm_PartitionedCall_transpose_perm, x = model_embedding_embedding_lookup_cast_fp16_to_fp32)[name = tensor<string, []>("transpose_4")];
            list<tensor<fp32, [1, 64]>, 256> model_lstm_PartitionedCall_TensorArrayUnstack_TensorListFromTensor = list_scatter(indices = range_1d_0, ls = tf_make_list_0, value = model_lstm_PartitionedCall_transpose)[name = tensor<string, []>("model_lstm_PartitionedCall_TensorArrayUnstack_TensorListFromTensor")];
            tensor<int32, []> model_lstm_PartitionedCall_strided_slice = const()[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice"), val = tensor<int32, []>(256)];
            tensor<int32, []> model_lstm_PartitionedCall_while_0, list<tensor<fp32, [1, 64]>, 1> model_lstm_PartitionedCall_while_1, tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_2, tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_3 = while_loop(loop_vars = (model_lstm_PartitionedCall_time, model_lstm_PartitionedCall_TensorArrayV2_1, model_lstm_zeros, model_lstm_zeros_1))[name = tensor<string, []>("model_lstm_PartitionedCall_while_renamed")]
                (tensor<int32, []> model_lstm_PartitionedCall_time_x0_1_1_1_0, list<tensor<fp32, [1, 64]>, 1> model_lstm_PartitionedCall_TensorArrayV2_1_x0, tensor<fp32, [1, 64]> model_lstm_zeros_x0_1_1_1_0, tensor<fp32, [1, 64]> model_lstm_zeros_1_x0_1_1_1_0) {
                    tensor<bool, []> model_lstm_PartitionedCall_while_while_cond_23725_while_Less = less(x = model_lstm_PartitionedCall_time_x0_1_1_1_0, y = model_lstm_PartitionedCall_strided_slice)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_cond_23725_while_Less")];
                } -> (model_lstm_PartitionedCall_while_while_cond_23725_while_Less)
                (tensor<int32, []> model_lstm_PartitionedCall_time_x0_1_1_1_1, list<tensor<fp32, [1, 64]>, 1> model_lstm_PartitionedCall_TensorArrayV2_1_x0_1, tensor<fp32, [1, 64]> model_lstm_zeros_x0_1_1_1_1, tensor<fp32, [1, 64]> model_lstm_zeros_1_x0_1_1_1_1) {
                    tensor<int32, []> model_lstm_PartitionedCall_while_while_body_23726_while_split_split_dim = const()[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_split_split_dim"), val = tensor<int32, []>(1)];
                    tensor<int32, []> model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Write_TensorListSetItem_index = const()[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Write_TensorListSetItem_index"), val = tensor<int32, []>(0)];
                    tensor<int32, []> model_lstm_PartitionedCall_while_while_body_23726_while_add_2_y = const()[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_add_2_y"), val = tensor<int32, []>(1)];
                    tensor<int32, []> model_lstm_PartitionedCall_while_while_body_23726_while_add_2 = add(x = model_lstm_PartitionedCall_time_x0_1_1_1_1, y = model_lstm_PartitionedCall_while_while_body_23726_while_add_2_y)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_add_2")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Read_TensorListGetItem = list_read(index = model_lstm_PartitionedCall_time_x0_1_1_1_1, ls = model_lstm_PartitionedCall_TensorArrayUnstack_TensorListFromTensor)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Read_TensorListGetItem")];
                    tensor<bool, []> model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1_transpose_x_1 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1_transpose_x_1"), val = tensor<bool, []>(false)];
                    tensor<bool, []> model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1_transpose_y_1 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1_transpose_y_1"), val = tensor<bool, []>(false)];
                    tensor<fp32, [1, 256]> model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1 = matmul(transpose_x = model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1_transpose_x_1, transpose_y = model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1_transpose_y_1, x = model_lstm_zeros_x0_1_1_1_1, y = Func_model_lstm_PartitionedCall_input__4)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1")];
                    tensor<bool, []> model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_transpose_x_1 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_transpose_x_1"), val = tensor<bool, []>(false)];
                    tensor<bool, []> model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_transpose_y_1 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_transpose_y_1"), val = tensor<bool, []>(false)];
                    tensor<fp32, [1, 256]> model_lstm_PartitionedCall_while_while_body_23726_while_MatMul = matmul(transpose_x = model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_transpose_x_1, transpose_y = model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_transpose_y_1, x = model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Read_TensorListGetItem, y = Func_model_lstm_PartitionedCall_input__3)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_MatMul")];
                    tensor<fp32, [1, 256]> model_lstm_PartitionedCall_while_while_body_23726_while_add = add(x = model_lstm_PartitionedCall_while_while_body_23726_while_MatMul, y = model_lstm_PartitionedCall_while_while_body_23726_while_MatMul_1)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_add")];
                    tensor<fp32, [1, 256]> model_lstm_PartitionedCall_while_while_body_23726_while_BiasAdd = add(x = model_lstm_PartitionedCall_while_while_body_23726_while_add, y = Func_model_lstm_PartitionedCall_input__5)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_BiasAdd")];
                    tensor<int32, []> model_lstm_PartitionedCall_while_while_body_23726_while_split_num_splits_1 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_split_num_splits_1"), val = tensor<int32, []>(4)];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_split_0, tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_split_1, tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_split_2, tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_split_3 = split(axis = model_lstm_PartitionedCall_while_while_body_23726_while_split_split_dim, num_splits = model_lstm_PartitionedCall_while_while_body_23726_while_split_num_splits_1, x = model_lstm_PartitionedCall_while_while_body_23726_while_BiasAdd)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_split")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid = sigmoid(x = model_lstm_PartitionedCall_while_while_body_23726_while_split_0)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid_1 = sigmoid(x = model_lstm_PartitionedCall_while_while_body_23726_while_split_1)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid_1")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_Tanh = tanh(x = model_lstm_PartitionedCall_while_while_body_23726_while_split_2)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_Tanh")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid_2 = sigmoid(x = model_lstm_PartitionedCall_while_while_body_23726_while_split_3)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid_2")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_mul = mul(x = model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid_1, y = model_lstm_zeros_1_x0_1_1_1_1)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_mul")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_mul_1 = mul(x = model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid, y = model_lstm_PartitionedCall_while_while_body_23726_while_Tanh)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_mul_1")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_add_1 = add(x = model_lstm_PartitionedCall_while_while_body_23726_while_mul, y = model_lstm_PartitionedCall_while_while_body_23726_while_mul_1)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_add_1")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_Tanh_1 = tanh(x = model_lstm_PartitionedCall_while_while_body_23726_while_add_1)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_Tanh_1")];
                    tensor<fp32, [1, 64]> model_lstm_PartitionedCall_while_while_body_23726_while_mul_2 = mul(x = model_lstm_PartitionedCall_while_while_body_23726_while_Sigmoid_2, y = model_lstm_PartitionedCall_while_while_body_23726_while_Tanh_1)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_mul_2")];
                    list<tensor<fp32, [1, 64]>, 1> model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Write_TensorListSetItem = list_write(index = model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Write_TensorListSetItem_index, ls = model_lstm_PartitionedCall_TensorArrayV2_1_x0_1, value = model_lstm_PartitionedCall_while_while_body_23726_while_mul_2)[name = tensor<string, []>("model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Write_TensorListSetItem")];
                } -> (model_lstm_PartitionedCall_while_while_body_23726_while_add_2, model_lstm_PartitionedCall_while_while_body_23726_while_TensorArrayV2Write_TensorListSetItem, model_lstm_PartitionedCall_while_while_body_23726_while_mul_2, model_lstm_PartitionedCall_while_while_body_23726_while_add_1);
            tensor<int32, [1]> range_1d_1 = const()[name = tensor<string, []>("range_1d_1"), val = tensor<int32, [1]>([0])];
            tensor<fp32, [1, 1, 64]> model_lstm_PartitionedCall_TensorArrayV2Stack_TensorListStack = list_gather(indices = range_1d_1, ls = model_lstm_PartitionedCall_while_1)[name = tensor<string, []>("model_lstm_PartitionedCall_TensorArrayV2Stack_TensorListStack")];
            tensor<int32, [3]> model_lstm_PartitionedCall_strided_slice_2_begin_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice_2_begin_0"), val = tensor<int32, [3]>([-1, 0, 0])];
            tensor<int32, [3]> model_lstm_PartitionedCall_strided_slice_2_end_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice_2_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> model_lstm_PartitionedCall_strided_slice_2_stride_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice_2_stride_0"), val = tensor<int32, [3]>([1, 1, 1])];
            tensor<bool, [3]> model_lstm_PartitionedCall_strided_slice_2_begin_mask_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice_2_begin_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> model_lstm_PartitionedCall_strided_slice_2_end_mask_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice_2_end_mask_0"), val = tensor<bool, [3]>([false, true, true])];
            tensor<bool, [3]> model_lstm_PartitionedCall_strided_slice_2_squeeze_mask_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice_2_squeeze_mask_0"), val = tensor<bool, [3]>([true, false, false])];
            tensor<fp32, [1, 64]> model_lstm_PartitionedCall_strided_slice_2 = slice_by_index(begin = model_lstm_PartitionedCall_strided_slice_2_begin_0, begin_mask = model_lstm_PartitionedCall_strided_slice_2_begin_mask_0, end = model_lstm_PartitionedCall_strided_slice_2_end_0, end_mask = model_lstm_PartitionedCall_strided_slice_2_end_mask_0, squeeze_mask = model_lstm_PartitionedCall_strided_slice_2_squeeze_mask_0, stride = model_lstm_PartitionedCall_strided_slice_2_stride_0, x = model_lstm_PartitionedCall_TensorArrayV2Stack_TensorListStack)[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice_2")];
            tensor<string, []> model_lstm_PartitionedCall_strided_slice_2_to_fp16_dtype_0 = const()[name = tensor<string, []>("model_lstm_PartitionedCall_strided_slice_2_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [256, 64]> transpose_1_to_fp16 = const()[name = tensor<string, []>("transpose_1_to_fp16"), val = tensor<fp16, [256, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1862784)))];
            tensor<fp16, [256]> model_dense_BiasAdd_bias_0_to_fp16 = const()[name = tensor<string, []>("model_dense_BiasAdd_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1895616)))];
            tensor<fp16, [1, 64]> model_lstm_PartitionedCall_strided_slice_2_to_fp16 = cast(dtype = model_lstm_PartitionedCall_strided_slice_2_to_fp16_dtype_0, x = model_lstm_PartitionedCall_strided_slice_2)[name = tensor<string, []>("cast_5")];
            tensor<fp16, [1, 256]> model_dense_BiasAdd_cast_fp16 = linear(bias = model_dense_BiasAdd_bias_0_to_fp16, weight = transpose_1_to_fp16, x = model_lstm_PartitionedCall_strided_slice_2_to_fp16)[name = tensor<string, []>("model_dense_BiasAdd_cast_fp16")];
            tensor<string, []> model_dense_BiasAdd_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("model_dense_BiasAdd_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 256]> model_dense_BiasAdd_cast_fp16_to_fp32 = cast(dtype = model_dense_BiasAdd_cast_fp16_to_fp32_dtype_0, x = model_dense_BiasAdd_cast_fp16)[name = tensor<string, []>("cast_4")];
            tensor<fp32, [1, 256]> model_dense_Relu = relu(x = model_dense_BiasAdd_cast_fp16_to_fp32)[name = tensor<string, []>("model_dense_Relu")];
            tensor<bool, []> model_concatenate_concat_interleave_0 = const()[name = tensor<string, []>("model_concatenate_concat_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 2512]> model_concatenate_concat = concat(axis = model_concatenate_concat_axis, interleave = model_concatenate_concat_interleave_0, values = (model_dense_Relu, model_tf_split_1_split_1, model_dense_1_Relu))[name = tensor<string, []>("model_concatenate_concat")];
            tensor<string, []> model_concatenate_concat_to_fp16_dtype_0 = const()[name = tensor<string, []>("model_concatenate_concat_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [256, 2512]> transpose_2_to_fp16 = const()[name = tensor<string, []>("transpose_2_to_fp16"), val = tensor<fp16, [256, 2512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1896192)))];
            tensor<fp16, [256]> model_dense_2_BiasAdd_bias_0_to_fp16 = const()[name = tensor<string, []>("model_dense_2_BiasAdd_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3182400)))];
            tensor<fp16, [1, 2512]> model_concatenate_concat_to_fp16 = cast(dtype = model_concatenate_concat_to_fp16_dtype_0, x = model_concatenate_concat)[name = tensor<string, []>("cast_3")];
            tensor<fp16, [1, 256]> model_dense_2_BiasAdd_cast_fp16 = linear(bias = model_dense_2_BiasAdd_bias_0_to_fp16, weight = transpose_2_to_fp16, x = model_concatenate_concat_to_fp16)[name = tensor<string, []>("model_dense_2_BiasAdd_cast_fp16")];
            tensor<string, []> model_dense_2_BiasAdd_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("model_dense_2_BiasAdd_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 256]> model_dense_2_BiasAdd_cast_fp16_to_fp32 = cast(dtype = model_dense_2_BiasAdd_cast_fp16_to_fp32_dtype_0, x = model_dense_2_BiasAdd_cast_fp16)[name = tensor<string, []>("cast_2")];
            tensor<fp32, [1, 256]> model_dense_2_Relu = relu(x = model_dense_2_BiasAdd_cast_fp16_to_fp32)[name = tensor<string, []>("model_dense_2_Relu")];
            tensor<string, []> model_dense_2_Relu_to_fp16_dtype_0 = const()[name = tensor<string, []>("model_dense_2_Relu_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [5, 256]> transpose_3_to_fp16 = const()[name = tensor<string, []>("transpose_3_to_fp16"), val = tensor<fp16, [5, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3182976)))];
            tensor<fp16, [5]> model_dense_3_BiasAdd_bias_0_to_fp16 = const()[name = tensor<string, []>("model_dense_3_BiasAdd_bias_0_to_fp16"), val = tensor<fp16, [5]>([0x1.3b4p-8, -0x1.388p-3, -0x1.518p-9, 0x1.b98p-3, -0x1.138p-8])];
            tensor<fp16, [1, 256]> model_dense_2_Relu_to_fp16 = cast(dtype = model_dense_2_Relu_to_fp16_dtype_0, x = model_dense_2_Relu)[name = tensor<string, []>("cast_1")];
            tensor<fp16, [1, 5]> model_dense_3_BiasAdd_cast_fp16 = linear(bias = model_dense_3_BiasAdd_bias_0_to_fp16, weight = transpose_3_to_fp16, x = model_dense_2_Relu_to_fp16)[name = tensor<string, []>("model_dense_3_BiasAdd_cast_fp16")];
            tensor<string, []> model_dense_3_BiasAdd_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("model_dense_3_BiasAdd_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> model_dense_3_Softmax_axis_0 = const()[name = tensor<string, []>("model_dense_3_Softmax_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp32, [1, 5]> model_dense_3_BiasAdd_cast_fp16_to_fp32 = cast(dtype = model_dense_3_BiasAdd_cast_fp16_to_fp32_dtype_0, x = model_dense_3_BiasAdd_cast_fp16)[name = tensor<string, []>("cast_0")];
            tensor<fp32, [1, 5]> Identity = softmax(axis = model_dense_3_Softmax_axis_0, x = model_dense_3_BiasAdd_cast_fp16_to_fp32)[name = tensor<string, []>("model_dense_3_Softmax")];
        } -> (Identity);
}