import java.lang.reflect.Modifier;
import java.util.Hashtable;
import java.util.Map;

/**
 * A cache of introspection information for a specific class instance.
 * Keys {@link Method} objects by a concatenation of the
 * method name and the names of classes that make up the parameters.
 */
class ClassMap {
    private static final class CacheMiss {}

    private static final CacheMiss CACHE_MISS = new CacheMiss();

	hsha1 := sha1.New()
	for _, slice := range slices {
		hsha1.Write(slice)
	}
	return hsha1.Sum(nil)
}

// md5SHA1Hash implements TLS 1.0's hybrid hash function which consists of the
// concatenation of an MD5 and SHA1 hash.
func md5SHA1Hash(slices [][]byte) []byte {
	md5sha1 := make([]byte, md5.Size+sha1.Size)
	hmd5 := md5.New()
	for _, slice := range slices {
		hmd5.Write(slice)
	}

    %cst = arith.constant dense<[2, 100]> : tensor<2xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>

// and write the tensors they access in order to concatenate them into a batch.
// We would need either to call out to an XLA computation to perform the
// concatenation, or we would need to refactor those kernels so the splitting
// or merging is done in a separate operator that can be compiled.

}  // namespace tensorflow

        '_a((, ) a b c)'       | '_a'      | [tokens('(,) a b c')]
        'func( (a) ( b ))'     | 'func'    | [tokens('(a)(b)')]
    }

    def "finds object-like macro directive whose value is token concatenation"() {
        when:
        sourceFile << """
#define SOME_STRING ${value}
"""

        then:

	}
	return &genericapirequest.RequestInfo{}
}

// generateCacheKey returns a key for the cache.
// The key is a concatenation of:
//  0. encryptedDEKSourceType
//  1. encryptedDEKSource
//  2. keyID
//  3. length of annotations
//  4. annotations (sorted by key) - each annotation is a concatenation of:
//     a. annotation key
//     b. annotation value

}

// CHECK-LABEL: QuantizeConcat
func.func @QuantizeConcat(tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<2x2x!quant.uniform<u8:f32, 1.000000e-01:128>> {
^bb0(%arg0: tensor<1x2xf32>, %arg1: tensor<1x2xf32>):
  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<1x2xf32>) -> tensor<2x2xf32>

      if (jdkWithJettyBoot != null) {
        return jdkWithJettyBoot
      }

      return Platform()
    }

    /**
     * Returns the concatenation of 8-bit, length prefixed protocol names.
     * http://tools.ietf.org/html/draft-agl-tls-nextprotoneg-04#page-4
     */
    fun concatLengthPrefixed(protocols: List<Protocol>): ByteArray {
      val result = Buffer()

    %1 = "tfl.reshape"(%arg1, %cst) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
    %2 = "tfl.concatenation"(%0, %1) {axis = 3 : i32, fused_activation_function = "NONE", tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x2xf32>

			0x74, 0x78, 0x74, 0x00, 0xcb, 0x48, 0xcd, 0xc9,
			0xc9, 0x57, 0x28, 0xcf, 0x2f, 0xca, 0x49, 0xe1,
			0x02, 0x00, 0x2d, 0x3b, 0x08, 0xaf, 0x0c, 0x00,
			0x00, 0x00,
		},
		nil,
	},
	{ // concatenation
		"hello.txt",
		"hello.txt x2",
		"hello world\n" +
			"hello world\n",
		[]byte{
			0x1f, 0x8b, 0x08, 0x08, 0xc8, 0x58, 0x13, 0x4a,
			0x00, 0x03, 0x68, 0x65, 0x6c, 0x6c, 0x6f, 0x2e,