np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(
                  name='x', shape=x_signature, dtype=dtypes.float32
              )
          ]
      )

	}
}

func TestSimpleError(t *testing.T) {
	testClientHelloFailure(t, testConfig, &serverHelloDoneMsg{}, "unexpected handshake message")
}

var badProtocolVersions = []uint16{0x0000, 0x0005, 0x0100, 0x0105, 0x0200, 0x0205, VersionSSL30}

func TestRejectBadProtocolVersion(t *testing.T) {
	config := testConfig.Clone()
	config.MinVersion = VersionSSL30
	for _, v := range badProtocolVersions {

      def __init__(self, bias: Optional[core.Tensor]):
        self._bias = bias
        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(
                  name='x', shape=shapes[0], dtype=dtypes.float32
              )
          ]
      )

    : TFL_Op<"bucketize", [Pure, SameOperandsAndResultShape]> {
  let summary = "Bucketizes 'input' based on 'boundaries'.";

  let description = [{
Example:

If the inputs are `boundaries = [0, 10, 100]` and
`input = [[-5, 10000][150, 10][5, 100]]`,
then the output will be `output = [[0, 3][3, 2][1, 3]]`.
  }];

  let arguments = (ins
    TFL_TensorOf<[F32, F64, I32, I64]>:$input,

	http2cipher_TLS_DHE_PSK_WITH_RC4_128_SHA             uint16 = 0x008E
	http2cipher_TLS_DHE_PSK_WITH_3DES_EDE_CBC_SHA        uint16 = 0x008F
	http2cipher_TLS_DHE_PSK_WITH_AES_128_CBC_SHA         uint16 = 0x0090
	http2cipher_TLS_DHE_PSK_WITH_AES_256_CBC_SHA         uint16 = 0x0091
	http2cipher_TLS_RSA_PSK_WITH_RC4_128_SHA             uint16 = 0x0092
	http2cipher_TLS_RSA_PSK_WITH_3DES_EDE_CBC_SHA        uint16 = 0x0093

def TF_BucketizeOp : TF_Op<"Bucketize", [Pure, SameOperandsAndResultShape]> {
  let summary = "Bucketizes 'input' based on 'boundaries'.";

  let description = [{
For example, if the inputs are
    boundaries = [0, 10, 100]
    input = [[-5, 10000]
             [150,   10]
             [5,    100]]

then the output will be
    output = [[0, 3]
              [3, 2]
              [1, 3]]
  }];