// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf | FileCheck --dump-input=always %s
// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf=use-fake-quant-num-bits=true | FileCheck --check-prefix LOBIT --dump-input=always %s

// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf | FileCheck --dump-input=always %s
// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf=use-fake-quant-num-bits=true | FileCheck --check-prefix LOBIT --dump-input=always %s

      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights_quant/max"
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  op: "FakeQuantWithMinMaxVarsPerChannel"
  input: "BoxPredictor_4/ClassPredictor/weights/read"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/min/read"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/max/read"

      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  op: "FakeQuantWithMinMaxVarsPerChannel"
  input: "BoxPredictor_4/ClassPredictor/weights/read"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/min"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/max"
  attr {

      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  op: "FakeQuantWithMinMaxVarsPerChannel"
  input: "BoxPredictor_4/ClassPredictor/weights/read"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/min"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/max"
  attr {

      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights_quant/max"
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  op: "FakeQuantWithMinMaxVarsPerChannel"
  input: "BoxPredictor_4/ClassPredictor/weights/read"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/min/read"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/max/read"

  %2 = "tf.Neg"(%1) {device = ""} : (tensor<4xf32>) -> tensor<4xf32>
  %3 = "tfl.custom_tf"(%cst, %2, %1) ({
  ^bb0(%arg1: tensor<*xf32>, %arg2: tensor<*xf32>, %arg3: tensor<*xf32>):
    %7 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg1, %arg2, %arg3) {device = "", narrow_range = false, num_bits = 8 : i64} : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    "tfl.yield"(%7) : (tensor<*xf32>) -> ()

*   Fixes a segfault in `QuantizedBiasAdd` ([CVE-2022-35972](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35972))
*   Fixes a `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannel` ([CVE-2022-36019](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-36019))
*   Fixes a segfault in `QuantizedMatMul` ([CVE-2022-35973](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35973))

    Res<TF_Float32Tensor, [{Backpropagated gradients w.r.t. max parameter:
`sum(gradients * (inputs > max))`.}]>:$backprop_wrt_max
  );
}

def TF_FakeQuantWithMinMaxVarsPerChannelOp : TF_Op<"FakeQuantWithMinMaxVarsPerChannel", [Pure]> {
  let summary = [{
Fake-quantize the 'inputs' tensor of type float via per-channel floats
  }];

  let description = [{