Arg<TF_FloatTensor, [{batch_size x num_classes matrix}]>:$features,
    Arg<TF_I32OrI64Tensor, [{batch_size vector with values in [0, num_classes).
This is the label for the given minibatch entry.}]>:$labels
  );

  let results = (outs
    Res<TF_FloatTensor, [{Per example loss (batch_size vector).}]>:$loss,
    Res<TF_FloatTensor, [{backpropagated gradients (batch_size x num_classes matrix).}]>:$backprop
  );

quantized_op = true, detections_per_class = 100 : i64, device = "", h_scale = 5.000000e+00 : f32, max_classes_per_detection = 1 : i64, max_detections = 20 : i64, nms_iou_threshold = 6.000000e-01 : f32, nms_score_threshold = 3.000000e-01 : f32, num_classes = 90 : i64, use_regular_nms = false, w_scale = 5.000000e+00 : f32, x_scale = 1.000000e+01 : f32, y_scale = 1.000000e+01 : f32} : (tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>) -> (tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>,...