%213 = "tf.Mul"(%212, %12) {device = ""} : (tensor<i64>, tensor<1xi64>) -> tensor<1xi64>
  %214 = "tf.Tile"(%213, %211) {device = ""} : (tensor<1xi64>, tensor<1xi64>) -> tensor<?xi64>
  %215 = "tf.Cumsum"(%214, %14) {device = "", exclusive = false, reverse = false} : (tensor<?xi64>, tensor<i32>) -> tensor<?xi64>

        TFL_TCresVTEtIsSameAsOp<0, 2>]>>,
    PredOpTrait<"bias must has num of elements equals to 4th dim of filter",
      Or<[
        TFL_OperandIsNoneType<2>,
        TFL_NumElementsEqualsDim<2, 1, 4>]>>]> {
  let summary = "Convolution 3D operator";

  let description = [{
    Performs convolution operation on 3D inputs.
    Inputs:
      `inputs[0]`: required: the input activation tensor

GoStart dt=4 g=51 g_seq=38
GoLabel dt=1 label_string=2
GoBlock dt=3928 reason_string=15 stack=5
GoUnblock dt=14 g=51 g_seq=39 stack=0
GoStart dt=3 g=51 g_seq=40
GoLabel dt=3 label_string=2
GoBlock dt=214 reason_string=15 stack=5
GoUnblock dt=5 g=51 g_seq=41 stack=0
GoStart dt=1 g=51 g_seq=42
GoLabel dt=1 label_string=2
GoBlock dt=305 reason_string=15 stack=5
GoUnblock dt=8 g=51 g_seq=43 stack=0

    *   This dependency affected anyone installing TensorFlow 2.15 alongside
        NVIDIA CUDA dependencies via `pip install tensorflow[and-cuda]`.
    *   Depending on the installation method, TensorFlow 2.14 would be installed
        instead of 2.15, or users could receive an installation error due to
        those missing dependencies.
*   TensorFlow 2.15.0.post1 is being released for Linux x86_64 to resolve this

func (*TypedLocalObjectReference) ProtoMessage() {}
func (*TypedLocalObjectReference) Descriptor() ([]byte, []int) {
	return fileDescriptor_6c07b07c062484ab, []int{214}
}
func (m *TypedLocalObjectReference) XXX_Unmarshal(b []byte) error {
	return m.Unmarshal(b)
}
func (m *TypedLocalObjectReference) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	b = b[:cap(b)]