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", "7.1"}, {"mldb_token", "mldb-wzxy8wby59"}})]
{
    func main<ios15>(tensor<fp32, [1, 2002]> x) {
            tensor<int32, [3]> model_tf_split_Const = const()[name = tensor<string, []>("model_tf_split_Const"), val = tensor<int32, [3]>([2000, 1, 1])];
            tensor<int32, []> model_tf_split_split_split_dim = const()[name = tensor<string, []>("model_tf_split_split_split_dim"), val = tensor<int32, []>(1)];
            tensor<int32, [2]> model_flatten_Const = const()[name = tensor<string, []>("model_flatten_Const"), val = tensor<int32, [2]>([-1, 128])];
            tensor<int32, []> model_concatenate_concat_axis = const()[name = tensor<string, []>("model_concatenate_concat_axis"), val = tensor<int32, []>(1)];
            tensor<fp32, [257]> model_batch_normalization_batchnorm_mul = const()[name = tensor<string, []>("model_batch_normalization_batchnorm_mul"), val = tensor<fp32, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [257]> model_batch_normalization_batchnorm_sub = const()[name = tensor<string, []>("model_batch_normalization_batchnorm_sub"), val = tensor<fp32, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1216)))];
            tensor<int32, []> model_tf_split_split_num_splits_0 = const()[name = tensor<string, []>("model_tf_split_split_num_splits_0"), val = tensor<int32, []>(3)];
            tensor<fp32, [1, 2000]> model_tf_split_split_0, tensor<fp32, [1, 1]> model_tf_split_split_1, tensor<fp32, [1, 1]> model_tf_split_split_2 = split(axis = model_tf_split_split_split_dim, num_splits = model_tf_split_split_num_splits_0, split_sizes = model_tf_split_Const, x = x)[name = tensor<string, []>("model_tf_split_split")];
            tensor<string, []> model_tf_split_split_0_to_fp16_dtype_0 = const()[name = tensor<string, []>("model_tf_split_split_0_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [128, 2000]> transpose_0_to_fp16 = const()[name = tensor<string, []>("transpose_0_to_fp16"), val = tensor<fp16, [128, 2000]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2368)))];
            tensor<fp16, [128]> model_dense_BiasAdd_bias_0_to_fp16 = const()[name = tensor<string, []>("model_dense_BiasAdd_bias_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514432)))];
            tensor<fp16, [1, 2000]> cast_9 = cast(dtype = model_tf_split_split_0_to_fp16_dtype_0, x = model_tf_split_split_0)[name = tensor<string, []>("cast_9")];
            tensor<fp16, [1, 128]> model_dense_BiasAdd_cast_fp16 = linear(bias = model_dense_BiasAdd_bias_0_to_fp16, weight = transpose_0_to_fp16, x = cast_9)[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<string, []> model_embedding_Cast_dtype_0 = const()[name = tensor<string, []>("model_embedding_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, [621, 128]> model_embedding_embedding_lookup_81025_to_fp16 = const()[name = tensor<string, []>("model_embedding_embedding_lookup_81025_to_fp16"), val = tensor<fp16, [621, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514752)))];
            tensor<int32, [1, 1]> cast_7 = cast(dtype = model_embedding_Cast_dtype_0, x = model_tf_split_split_2)[name = tensor<string, []>("cast_7")];
            tensor<fp16, [1, 1, 128]> model_embedding_embedding_lookup_cast_fp16 = gather(axis = model_embedding_embedding_lookup_axis_0, indices = cast_7, x = model_embedding_embedding_lookup_81025_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<fp32, [1, 128]> cast_8 = cast(dtype = model_dense_BiasAdd_cast_fp16_to_fp32_dtype_0, x = model_dense_BiasAdd_cast_fp16)[name = tensor<string, []>("cast_8")];
            tensor<fp32, [1, 128]> model_dense_Relu = relu(x = cast_8)[name = tensor<string, []>("model_dense_Relu")];
            tensor<fp32, [1, 1, 128]> cast_6 = cast(dtype = model_embedding_embedding_lookup_cast_fp16_to_fp32_dtype_0, x = model_embedding_embedding_lookup_cast_fp16)[name = tensor<string, []>("cast_6")];
            tensor<fp32, [1, 128]> model_flatten_Reshape = reshape(shape = model_flatten_Const, x = cast_6)[name = tensor<string, []>("model_flatten_Reshape")];
            tensor<bool, []> model_concatenate_concat_interleave_0 = const()[name = tensor<string, []>("model_concatenate_concat_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 257]> model_concatenate_concat = concat(axis = model_concatenate_concat_axis, interleave = model_concatenate_concat_interleave_0, values = (model_dense_Relu, model_tf_split_split_1, model_flatten_Reshape))[name = tensor<string, []>("model_concatenate_concat")];
            tensor<fp32, [1, 257]> model_batch_normalization_batchnorm_mul_1 = mul(x = model_concatenate_concat, y = model_batch_normalization_batchnorm_mul)[name = tensor<string, []>("model_batch_normalization_batchnorm_mul_1")];
            tensor<fp32, [1, 257]> model_batch_normalization_batchnorm_add_1 = add(x = model_batch_normalization_batchnorm_mul_1, y = model_batch_normalization_batchnorm_sub)[name = tensor<string, []>("model_batch_normalization_batchnorm_add_1")];
            tensor<string, []> model_batch_normalization_batchnorm_add_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("model_batch_normalization_batchnorm_add_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [128, 257]> transpose_1_to_fp16 = const()[name = tensor<string, []>("transpose_1_to_fp16"), val = tensor<fp16, [128, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673792)))];
            tensor<fp16, [128]> model_dense_1_BiasAdd_bias_0_to_fp16 = const()[name = tensor<string, []>("model_dense_1_BiasAdd_bias_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(739648)))];
            tensor<fp16, [1, 257]> cast_5 = cast(dtype = model_batch_normalization_batchnorm_add_1_to_fp16_dtype_0, x = model_batch_normalization_batchnorm_add_1)[name = tensor<string, []>("cast_5")];
            tensor<fp16, [1, 128]> model_dense_1_BiasAdd_cast_fp16 = linear(bias = model_dense_1_BiasAdd_bias_0_to_fp16, weight = transpose_1_to_fp16, x = cast_5)[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, 128]> cast_4 = cast(dtype = model_dense_1_BiasAdd_cast_fp16_to_fp32_dtype_0, x = model_dense_1_BiasAdd_cast_fp16)[name = tensor<string, []>("cast_4")];
            tensor<fp32, [1, 128]> model_dense_1_Relu = relu(x = cast_4)[name = tensor<string, []>("model_dense_1_Relu")];
            tensor<string, []> model_dense_1_Relu_to_fp16_dtype_0 = const()[name = tensor<string, []>("model_dense_1_Relu_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [64, 128]> transpose_2_to_fp16 = const()[name = tensor<string, []>("transpose_2_to_fp16"), val = tensor<fp16, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(739968)))];
            tensor<fp16, [64]> model_dense_2_BiasAdd_bias_0_to_fp16 = const()[name = tensor<string, []>("model_dense_2_BiasAdd_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756416)))];
            tensor<fp16, [1, 128]> cast_3 = cast(dtype = model_dense_1_Relu_to_fp16_dtype_0, x = model_dense_1_Relu)[name = tensor<string, []>("cast_3")];
            tensor<fp16, [1, 64]> model_dense_2_BiasAdd_cast_fp16 = linear(bias = model_dense_2_BiasAdd_bias_0_to_fp16, weight = transpose_2_to_fp16, x = cast_3)[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, 64]> cast_2 = 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, 64]> model_dense_2_Relu = relu(x = cast_2)[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, [1, 64]> transpose_3_to_fp16 = const()[name = tensor<string, []>("transpose_3_to_fp16"), val = tensor<fp16, [1, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(756608)))];
            tensor<fp16, [1]> model_dense_3_BiasAdd_bias_0_to_fp16 = const()[name = tensor<string, []>("model_dense_3_BiasAdd_bias_0_to_fp16"), val = tensor<fp16, [1]>([-0x1.6a4p-3])];
            tensor<fp16, [1, 64]> cast_1 = cast(dtype = model_dense_2_Relu_to_fp16_dtype_0, x = model_dense_2_Relu)[name = tensor<string, []>("cast_1")];
            tensor<fp16, [1, 1]> model_dense_3_BiasAdd_cast_fp16 = linear(bias = model_dense_3_BiasAdd_bias_0_to_fp16, weight = transpose_3_to_fp16, x = cast_1)[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<fp32, [1, 1]> cast_0 = 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, 1]> Identity = sigmoid(x = cast_0)[name = tensor<string, []>("model_dense_3_Sigmoid")];
        } -> (Identity);
}