%0 = "tf.Conv3D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1, 1]} : (tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
  func.return %0: tensor<?x?x?x?x?xf32>

  // CHECK-LABEL: conv3d_valid
  // CHECK:  %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none

        %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

    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>

}

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

    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);

`channels_last_format`, see below for details.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_Conv2DOp : TF_Op<"Conv2D", [InferTensorType, Pure, TF_LayoutSensitiveInterface]> {
  let summary = [{
Computes a 2-D convolution given 4-D `input` and `filter` tensors.
  }];

  let description = [{