// have a corresponding 'depth_multiplier' attribute; the multiplier is the
    // fourth dimension in the 4-D filter tensor. We query the multiplier from
    // tf.DepthwiseConv2dNative and set it as the attribute value accordingly.
    auto multiplier =
        mlir::cast<RankedTensorType>(filter.getType()).getDimSize(3);

    filter = legalizeFilter(rewriter, loc, filter);

                $w_factor, TFL_AF_None, $padding, $stride_h, $stride_w,
                $multiplier)),
    (TFL_DepthwiseConv2DOp $input, $filter, $bias, $h_factor, $w_factor,
        ActFnAttr, $padding, $stride_h, $stride_w, $multiplier),
    [(HasOneUse $conv_out)]>;
}

multiclass FuseActFnIntoPoolOpPat<Op ActFnOp, ConstantStrAttr ActFnAttr> {

                        parameters.multiplier.set(2)
                    }
                    registerTransform(MakeColor) {
                        from.attribute(color, 'red')
                        to.attribute(color, 'green')
                        parameters.targetColor.set('green')
                        parameters.multiplier.set(1)
                    }
                }
            }

    int64_t in_channels = input_shape[3];
    // If in_channels is 1, there is no need to restore weight shape.
    if (in_channels == 1) return failure();
    int64_t multiplier = weight_shape[3] / in_channels;

    TensorType new_shape = RankedTensorType::get(
        {weight_shape[0], weight_shape[1], in_channels, multiplier},
        weight_type.getElementType());

    static final int MULTIPLIER = 1682554634;
    static final int SHIFT = Integer.numberOfLeadingZeros(TABLE.length() - 1);

    static final CharMatcher INSTANCE = new Whitespace();

    Whitespace() {
      super("CharMatcher.whitespace()");
    }

    @Override
    public boolean matches(char c) {
      return TABLE.charAt((MULTIPLIER * c) >>> SHIFT) == c;
    }

    static final int MULTIPLIER = 1682554634;
    static final int SHIFT = Integer.numberOfLeadingZeros(TABLE.length() - 1);

    static final CharMatcher INSTANCE = new Whitespace();

    Whitespace() {
      super("CharMatcher.whitespace()");
    }

    @Override
    public boolean matches(char c) {
      return TABLE.charAt((MULTIPLIER * c) >>> SHIFT) == c;
    }

                  of priority in case of multiple matches. A pool with no priority
                  set will be used only if the pools with priority can't be used.
                  If multiple matching IPAddressPools are available it will check
                  for the availability of IPs sorting the matching IPAddressPools
                  by priority, starting from the highest to the lowest. If multiple

    /**
     * A helper method to determine the value to resume the build with {@code -rf} taking into account the edge case
     *   where multiple modules in the reactor have the same artifactId.
     * <p>
     * {@code -rf :artifactId} will pick up the first module which matches, but when multiple modules in the reactor
     *   have the same artifactId, effective failed module might be later in build reactor.

  - On artifact foo-1.0.jar (org:foo:1.0) multiple problems reported:
      - in repository 'maven': artifact was signed but all keys were ignored
      - in repository 'maven': checksum is missing from verification metadata.
  - On artifact foo-1.0.pom (org:foo:1.0) multiple problems reported:
      - in repository 'maven': artifact was signed but all keys were ignored

		t,
		"testdata/valid-configs/aes/aes-cbc-multiple-providers.json",
		"k8s:enc:aescbc:v1:1:",
		"testdata/valid-configs/aes/aes-cbc-multiple-providers-reversed.json",
		"k8s:enc:aescbc:v1:2:",
	)
}

func TestCBCKeyRotationWithoutOverlappingProviders(t *testing.T) {
	testCBCKeyRotationWithProviders(
		t,
		"testdata/valid-configs/aes/aes-cbc-multiple-keys.json",
		"k8s:enc:aescbc:v1:A:",