x = unsafe.Pointer(&staticuint64s[val])
		if goarch.BigEndian {
			x = add(x, 6)
		}
	} else {
		x = mallocgc(2, uint16Type, false)
		*(*uint16)(x) = val
	}
	return
}

func convT32(val uint32) (x unsafe.Pointer) {
	if val < uint32(len(staticuint64s)) {
		x = unsafe.Pointer(&staticuint64s[val])
		if goarch.BigEndian {
			x = add(x, 4)
		}
	} else {
		x = mallocgc(4, uint32Type, false)

*   Fixes a null pointer dereference via invalid Ragged Tensors
    ([CVE-2021-29516](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29516))
*   Fixes a division by zero in `Conv3D`
    ([CVE-2021-29517](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-29517))
*   Fixes vulnerabilities where session operations in eager mode lead to null
    pointer dereferences

        %device_launch = "tf_device.cluster_func"(%input,...) {func = @_func,...)
        return ...
      }
      func @_func(%input: tensor<2x224x224x3xf32>, %filter: tensor<7x7x3x64xf32>) {
        %6 = "tf.Conv2D"(%input, %filter)  {strides = [1, 2, 2, 1]}: (tensor<2x230x230x3xf32>, tensor<7x7x3x64xf32>) -> tensor<2x112x112x64xf32>
      }
    }
    ```

    The program will be transformed into:

    ```mlir

}

type RawSockaddrL2TPIP struct {
	Family  uint16
	Unused  uint16
	Addr    [4]byte /* in_addr */
	Conn_id uint32
	_       [4]uint8
}

type RawSockaddrL2TPIP6 struct {
	Family   uint16
	Unused   uint16
	Flowinfo uint32
	Addr     [16]byte /* in6_addr */
	Scope_id uint32
	Conn_id  uint32
}

type RawSockaddrIUCV struct {
	Family  uint16
	Port    uint16
	Addr    uint32

    if (add.getType() != op.getType()) {
      add = rewriter.create<tensor::CastOp>(loc, op.getType(), add);
    }
    rewriter.replaceOp(op, {add});
    return success();
  }
};

// Conterts tf.Conv2D to mhlo.dynamic_conv.
// TODO(disc): To recover static special case's performance with adding folding,
// canonicalization func and removing ConvertConvOp.
template <typename OpT, int num_spatial_dims, bool depthwise_conv = false>

    return emitOptionalError(location, "invalid padding format provided");
  }

  // Output always have rank 4. All dimensions are initialized to
  // dynamic size and can be partially inferred.
  // TFL's conv2d is always NHWC format & the filter is OHWI.
  SmallVector<int64_t, 4> return_shape(4, ShapedType::kDynamic);
  return_shape[0] = input_ty.getDimSize(0);
  return_shape[3] = filter_ty.getDimSize(0);

w.r.t. the input of the convolution.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_Conv3DOp : TF_Op<"Conv3D", [InferTensorType, Pure]> {
  let summary = [{
Computes a 3-D convolution given 5-D `input` and `filter` tensors.
  }];

  let description = [{