program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3505.3.2"}, {"coremlc-version", "3505.4.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.2"}, {"mldb_token", "mldb-7mar5o8y6j"}})]
{
    func main<ios15>(tensor<fp32, [1, 512]> embedding) {
            tensor<string, []> embedding_to_fp16_dtype_0 = const()[name = tensor<string, []>("embedding_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [256, 512]> base_model_classifier_0_weight_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_0_weight_to_fp16"), val = tensor<fp16, [256, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [256]> base_model_classifier_0_bias_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262272)))];
            tensor<fp16, [1, 512]> embedding_to_fp16 = cast(dtype = embedding_to_fp16_dtype_0, x = embedding)[name = tensor<string, []>("cast_2")];
            tensor<fp16, [1, 256]> linear_0_cast_fp16 = linear(bias = base_model_classifier_0_bias_to_fp16, weight = base_model_classifier_0_weight_to_fp16, x = embedding_to_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
            tensor<int32, [1]> input_3_rank2_expansion_axes_0 = const()[name = tensor<string, []>("input_3_rank2_expansion_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 256, 1]> input_3_rank2_expansion_cast_fp16 = expand_dims(axes = input_3_rank2_expansion_axes_0, x = linear_0_cast_fp16)[name = tensor<string, []>("input_3_rank2_expansion_cast_fp16")];
            tensor<fp16, [256]> base_model_classifier_1_running_mean_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262848)))];
            tensor<fp16, [256]> base_model_classifier_1_running_var_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263424)))];
            tensor<fp16, [256]> base_model_classifier_1_weight_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264000)))];
            tensor<fp16, [256]> base_model_classifier_1_bias_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264576)))];
            tensor<fp16, []> var_4_to_fp16 = const()[name = tensor<string, []>("op_4_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 256, 1]> input_3_batch_norm_1d_cast_fp16 = batch_norm(beta = base_model_classifier_1_bias_to_fp16, epsilon = var_4_to_fp16, gamma = base_model_classifier_1_weight_to_fp16, mean = base_model_classifier_1_running_mean_to_fp16, variance = base_model_classifier_1_running_var_to_fp16, x = input_3_rank2_expansion_cast_fp16)[name = tensor<string, []>("input_3_batch_norm_1d_cast_fp16")];
            tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 256]> input_3_cast_fp16 = squeeze(axes = input_3_axes_0, x = input_3_batch_norm_1d_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
            tensor<fp16, [1, 256]> input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
            tensor<fp16, [128, 256]> base_model_classifier_4_weight_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_4_weight_to_fp16"), val = tensor<fp16, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265152)))];
            tensor<fp16, [128]> base_model_classifier_4_bias_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_4_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330752)))];
            tensor<fp16, [1, 128]> linear_1_cast_fp16 = linear(bias = base_model_classifier_4_bias_to_fp16, weight = base_model_classifier_4_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
            tensor<int32, [1]> input_11_rank2_expansion_axes_0 = const()[name = tensor<string, []>("input_11_rank2_expansion_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 128, 1]> input_11_rank2_expansion_cast_fp16 = expand_dims(axes = input_11_rank2_expansion_axes_0, x = linear_1_cast_fp16)[name = tensor<string, []>("input_11_rank2_expansion_cast_fp16")];
            tensor<fp16, [128]> base_model_classifier_5_running_mean_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_5_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331072)))];
            tensor<fp16, [128]> base_model_classifier_5_running_var_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_5_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331392)))];
            tensor<fp16, [128]> base_model_classifier_5_weight_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_5_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331712)))];
            tensor<fp16, [128]> base_model_classifier_5_bias_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_5_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332032)))];
            tensor<fp16, [1, 128, 1]> input_11_batch_norm_1d_cast_fp16 = batch_norm(beta = base_model_classifier_5_bias_to_fp16, epsilon = var_4_to_fp16, gamma = base_model_classifier_5_weight_to_fp16, mean = base_model_classifier_5_running_mean_to_fp16, variance = base_model_classifier_5_running_var_to_fp16, x = input_11_rank2_expansion_cast_fp16)[name = tensor<string, []>("input_11_batch_norm_1d_cast_fp16")];
            tensor<int32, [1]> input_11_axes_0 = const()[name = tensor<string, []>("input_11_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 128]> input_11_cast_fp16 = squeeze(axes = input_11_axes_0, x = input_11_batch_norm_1d_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
            tensor<fp16, [1, 128]> input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
            tensor<fp16, [3, 128]> base_model_classifier_8_weight_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_8_weight_to_fp16"), val = tensor<fp16, [3, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332352)))];
            tensor<fp16, [3]> base_model_classifier_8_bias_to_fp16 = const()[name = tensor<string, []>("base_model_classifier_8_bias_to_fp16"), val = tensor<fp16, [3]>([-0x1.528p-4, 0x1.4bp-4, 0x1.134p-5])];
            tensor<fp16, [1, 3]> linear_2_cast_fp16 = linear(bias = base_model_classifier_8_bias_to_fp16, weight = base_model_classifier_8_weight_to_fp16, x = input_13_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
            tensor<string, []> linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("linear_2_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> var_37 = const()[name = tensor<string, []>("op_37"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 3]> probs_cast_fp16 = softmax(axis = var_37, x = linear_2_cast_fp16)[name = tensor<string, []>("probs_cast_fp16")];
            tensor<string, []> probs_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("probs_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> preds_axis_0 = const()[name = tensor<string, []>("preds_axis_0"), val = tensor<int32, []>(1)];
            tensor<bool, []> preds_keep_dims_0 = const()[name = tensor<string, []>("preds_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<int32, [1]> preds = reduce_argmax(axis = preds_axis_0, keep_dims = preds_keep_dims_0, x = probs_cast_fp16)[name = tensor<string, []>("preds_cast_fp16")];
            tensor<int32, [1]> reduce_max_0_axes_0 = const()[name = tensor<string, []>("reduce_max_0_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> reduce_max_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_max_0_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1]> reduce_max_0_cast_fp16 = reduce_max(axes = reduce_max_0_axes_0, keep_dims = reduce_max_0_keep_dims_0, x = probs_cast_fp16)[name = tensor<string, []>("reduce_max_0_cast_fp16")];
            tensor<fp16, []> var_63_to_fp16 = const()[name = tensor<string, []>("op_63_to_fp16"), val = tensor<fp16, []>(0x1.cccp-1)];
            tensor<bool, [1]> var_64_cast_fp16 = greater_equal(x = reduce_max_0_cast_fp16, y = var_63_to_fp16)[name = tensor<string, []>("op_64_cast_fp16")];
            tensor<int32, [1]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> thresholded_preds = select(a = preds, b = expand_dims_0, cond = var_64_cast_fp16)[name = tensor<string, []>("op_65")];
            tensor<fp32, [1, 3]> probs = cast(dtype = probs_cast_fp16_to_fp32_dtype_0, x = probs_cast_fp16)[name = tensor<string, []>("cast_0")];
            tensor<fp32, [1, 3]> logits = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = tensor<string, []>("cast_1")];
        } -> (logits, probs, preds, thresholded_preds);
}