}

func.func @conv2d_backprop_input(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> {
  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>

  return %result : tensor<?x512x512x1xf32>
}

// -----

// CHECK-LABEL: @conv2d_backprop_input
func.func @conv2d_backprop_input(
    %filter: tensor<3x3x1x32xf32>,
    %out_backprop: tensor<100x26x26x32xf32>
  ) -> tensor<100x28x28x1xf32> {
    // CHECK: %[[REV_FILTER:.*]] = "mhlo.reverse"(%arg0) <{dimensions = dense<[0, 1]> : tensor<2xi64>}>

}

// CHECK-LABEL: conv2d_backprop_input_with_add
func.func @conv2d_backprop_input_with_add(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> {
  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>

}

// CHECK-LABEL: conv2d_backprop_input_with_add
func.func @conv2d_backprop_input_with_add(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> {
  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>

// CHECK:            %[[VAL_6:.*]] = "tf.Const"() <{value = dense<[1, 128, 128, 64]> : tensor<4xi32>}> : () -> tensor<4xi32>

*   Fixes a `CHECK` fail in `AvgPoolGrad` ([CVE-2022-35968](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35968))
*   Fixes a `CHECK` fail in `Conv2DBackpropInput` ([CVE-2022-35969](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35969))
*   Fixes a segfault in `QuantizedInstanceNorm` ([CVE-2022-35970](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35970))

    std::vector<int64_t> explicit_paddings;
    if (padding == tensorflow::Padding::EXPLICIT) {
      // EXPLICIT padding mode and the associated attribute is limited to
      // Conv2DBackpropInput. So, fetch attribute by identifier instead of the
      // op.explicit_paddings() attribute getter.
      ArrayRef<Attribute> explicit_paddings_attr =

the `filter` input of the convolution.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_Conv2DBackpropInputOp : TF_Op<"Conv2DBackpropInput", [Pure, TF_LayoutSensitiveInterface]> {
  let summary = [{
Computes the gradients of convolution with respect to the input.
  }];

  let arguments = (ins