noBuildTriggered()

        when:
        sourceFile.text = "class Thing {}"

        then:
        buildTriggeredAndSucceeded()
    }

    // Just exercises the dependency management layers to shake out any weirdness
    @Requires(UnitTestPreconditions.Online)
    def "can resolve dependencies from remote repository"() {
        when:
        def sourceFile = file("src/main/java/Thing.java") << "class Thing {}"

	}
	return c, kyberSharedSecret(ss, c), nil
}

func kyberSharedSecret(K, c []byte) []byte {
	// Package mlkem768 implements ML-KEM, which compared to Kyber removed a
	// final hashing step. Compute SHAKE-256(K || SHA3-256(c), 32) to match Kyber.
	// See https://words.filippo.io/mlkem768/#bonus-track-using-a-ml-kem-implementation-as-kyber-v3.
	h := sha3.NewShake256()
	h.Write(K)
	ch := sha3.Sum256(c)
	h.Write(ch[:])

	HasSHA1   bool // K{I,L}MD-SHA-1 functions
	HasSHA256 bool // K{I,L}MD-SHA-256 functions
	HasSHA512 bool // K{I,L}MD-SHA-512 functions
	HasSHA3   bool // K{I,L}MD-SHA3-{224,256,384,512} and K{I,L}MD-SHAKE-{128,256} functions
	HasVX     bool // vector facility. Note: the runtime sets this when it processes auxv records.
	HasVXE    bool // vector-enhancements facility 1
	HasKDSA   bool // elliptic curve functions

	if r, ok := w.(*response); ok {
		return r.conn.rwc, true
	}
	return nil, false
}

func init() {
	// Set the default rstAvoidanceDelay to the minimum possible value to shake
	// out tests that unexpectedly depend on it. Such tests should use
	// runTimeSensitiveTest and SetRSTAvoidanceDelay to explicitly raise the delay
	// if needed.
	rstAvoidanceDelay = 1 * time.Nanosecond
}

    value {
      list {
        shape {
          dim {
            size: 1
          }
          dim {
            size: 8
          }
        }
        shape {
          dim {
            size: 2
          }
          dim {
            size: -1
          }
          dim {
            size: 16
          }
        }
        shape {
          unknown_rank: true
        }

// Verifies that constants generated post shape inference are propagated.
// get_shape result in this test.
module attributes {tf.versions = {producer = 179 : i32}} {

  // CHECK-LABEL: func @main
  func.func @main(%arg0: tensor<10x19xf32>, %arg1: tensor<19x10xf32> {mhlo.is_same_data_across_replicas = true}) -> tensor<?xi64> {
    %0 = "tf.Shape"(%arg0) : (tensor<10x19xf32>) -> tensor<2xi64>

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-tpu-annotate-dynamic-shape-inputs %s | FileCheck %s

// Test that annotate the inputs of the cluster func to be dynamic shaped.

module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
   func.func @main(
      %arg0: tensor<2048xi64> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"},

Tres Popp <******@****.***> 1648061556 -0700

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME:   {{.*}})
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key2"]


def Test():

  x = tf.constant(1.0, shape=(3, 3))
  y = tf.constant(1.0, shape=(3, 3))

  s = tf.transpose(x)
  t = tf.transpose(y)

  tensor_info_s = tf.compat.v1.saved_model.utils.build_tensor_info(s)
  tensor_info_t = tf.compat.v1.saved_model.utils.build_tensor_info(t)

Tres Popp <******@****.***> 1648061556 -0700