}
}

LogicalResult SpaceToBatchNDOp::verify() {
  SpaceToBatchNDOp op = *this;
  const auto input_type = op.getInput().getType().cast<TensorType>();
  const auto block_shape_type = op.getBlockShape().getType().cast<TensorType>();
  const auto paddings_type = op.getPaddings().getType().cast<TensorType>();

  // Check that block_shape has rank 1.
  if (!IsOfRankOrUnranked(op.getBlockShape(), 1)) {

  auto input_args_have_static_shape = [&]() -> bool {
    return mlir::cast<TensorType>(input_tensor.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(kernel_tensor.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(window_strides.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(padding.getType()).hasStaticShape() &&
           mlir::cast<TensorType>(lhs_dilation.getType()).hasStaticShape() &&

          CPred<"$_op.getOperand(0).getType().cast<ShapedType>().getRank() <= 1">,
          CPred<"$_op.getOperand(0).getType().cast<ShapedType>().getRank() == 2 && !$_op.getOperand(0).getType().cast<ShapedType>().hasStaticShape()">,
          CPred<"$_op.getOperand(0).getType().cast<ShapedType>().getRank() == 2 && $_op.getOperand(0).getType().cast<ShapedType>().getShape()[1] <= 4">]>>]> {

// CHECK-LABEL: Tanh
// CHECK:  "tfl.tanh"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
}

func.func @cast(%arg0: tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> {
  %0 = "tf.Cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
  func.return %0 : tensor<1x2x2x5xf32>

  // CHECK-LABEL: cast
  // CHECK: "tfl.cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
}

    // CHECK-SAME: (tensor<*xf32>) -> tensor<*xi8>
    // CHECK: %[[TENSOR_CAST:.*]] = tensor.cast %[[CAST_RESULT_2]] : tensor<*xi8> to tensor<?x?x?x?xi8>
    %2 = tensor.cast %28 : tensor<*xi8> to tensor<?x?x?x?xi8>
    // CHECK: %[[CAST_RESULT_0:.*]] = "tf.Cast"(%arg0)
    // CHECK-SAME: (tensor<16x194x199x4xf32>) -> tensor<16x194x199x4xui8>
    // CHECK: %[[CAST_RESULT_1:.*]] = "tf.Cast"(%arg0)

static LogicalResult Verify(OpT op) {
  if (!HasRankAtLeast(op.getX(), 2)) {
    return op.emitOpError("requires lhs operand to have rank at least two");
  }
  if (!HasRankAtLeast(op.getY(), 2)) {
    return op.emitOpError("requires rhs operand to have rank at least two");
  }

  RankedTensorType x_ty = GetRankedTensorTypeForOperand(op.getX());
  RankedTensorType y_ty = GetRankedTensorTypeForOperand(op.getY());

    tar xzf easy-rsa.tar.gz
    mkdir easy-rsa-master/kubelet
    cp -r easy-rsa-master/easyrsa3/* easy-rsa-master/kubelet
    mkdir easy-rsa-master/aggregator
    cp -r easy-rsa-master/easyrsa3/* easy-rsa-master/aggregator
    mkdir easy-rsa-master/cloud-pvl-admission
    cp -r easy-rsa-master/easyrsa3/* easy-rsa-master/cloud-pvl-admission

  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<8x16x32x64xf32>) -> tensor<8x16x32x64xi32>
  %1 = "tf.Cast"(%arg0) {Truncate = true} : (tensor<8x16x32x64xf32>) -> tensor<8x16x32x64xi32>
  func.return %0, %1: tensor<8x16x32x64xi32>, tensor<8x16x32x64xi32>

  // CHECK: %0 = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<8x16x32x64xf32>) -> tensor<8x16x32x64xi32>

		}
		value, ok := cfg.Spec.(*networking.Gateway)
		if !ok {
			v = AppendValidation(v, fmt.Errorf("cannot cast to gateway: %#v", cfg.Spec))
			return v.Unwrap()
		}

		if len(value.Servers) == 0 {
			v = AppendValidation(v, fmt.Errorf("gateway must have at least one server"))
		} else {
			for _, server := range value.Servers {
				v = AppendValidation(v, validateServer(server, gatewaySemantics))

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