*   Fixes a missing validation which causes denial of service via `Conv3DBackpropFilterV2` ([CVE-2022-29196](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-29196))
*   Fixes a `CHECK` failure in depthwise ops via overflows ([CVE-2021-41197](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-41197))

^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x4xf32>) :
  %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {device = "", name = "MobilenetV2/expanded_conv/depthwise/depthwise", T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x30x30x12xf32>

    const int64_t filter_channels = GetDimSize(filter_ty, num_spatial_dims);
    // TensorFlow convolution op verifies that the number of input channels is
    // divisible by the number of filter channels.
    // For depthwise convolution the feature_group_count argument would be set
    // to the input feature dimension.
    const int64_t feature_group_count =
        depthwise_conv ? input_channels : input_channels / filter_channels;

                shape=[
                    tensor_shape_pb2.TensorShapeProto(
                        dim=[
                            tensor_shape_pb2.TensorShapeProto.Dim(
                                # Depthwise conv is reshaped to [H,W,1,CxM].
                                size=filter_shape[quantized_axis]
                                * filter_shape[2]
                            )
                        ]

  );

  let results = (outs TFL_TensorOf<[F32, I32, I64]>:$output);

  let hasOptions = 1;
}

def TFL_DepthwiseConv2DOp :
    TFL_ConvOp<"depthwise_conv_2d", "Depthwise-separable convolution", 3,
                [DeclareOpInterfaceMethods<TFL_ArithmeticCount>,
                 DynamicRangeQuantizedOpInterface]> {
  let arguments = (
    ins TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$input,

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_DepthwiseConv2dNativeOp : TF_Op<"DepthwiseConv2dNative", [Pure]> {
  let summary = [{
Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
  }];

  let description = [{
Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
and a filter / kernel tensor of shape