"ML\EFR\0DStableHLO_v0.17.1\00\01!\05\01\05\11\01\03\0B\03\0F\0F\13\17\1B\1F#'\03\7FQ\1B\01'\0B\13\0B\0F\13\13\13\13\13\13\13\13\13\0B\13\13\0B\13\13\03+\0B\0B\0B\0F\0B\0B\0B\1F\0F\0B\0B\0B\0B\0B\0B\1B\0B\0B/\13/\01\03\0B\03\19\0F\07\07;\1B7\13\1F\07\0F\07\13\02\22\03\05\15\03\03\05\07\05\17\11\01\01\17\01!\15\17\01\05\07\17\01\05c\17\01\05\8B\17\01\05\C1\17\01\07\15\17\01\09\15\17\01\0B\15\03\03\1B=\05\19\17\01\0D\0B\03\03!E\05\1B\17\01\0F\15\17\01\17\0B\03\01\1D\1D\1D\1F\1F\19\01\17\01#\11\1D!\1...

	Pq   = 0xff // both escapes: 66 0f
	Pb   = 0xfe // byte operands
	Pf2  = 0xf2 // xmm escape 1: f2 0f
	Pf3  = 0xf3 // xmm escape 2: f3 0f
	Pef3 = 0xf5 // xmm escape 2 with 16-bit prefix: 66 f3 0f
	Pq3  = 0x67 // xmm escape 3: 66 48 0f
	Pq4  = 0x68 // xmm escape 4: 66 0F 38
	Pq4w = 0x69 // Pq4 with Rex.w 66 0F 38
	Pq5  = 0x6a // xmm escape 5: F3 0F 38
	Pq5w = 0x6b // Pq5 with Rex.w F3 0F 38

    // Attributes
    DefaultValuedStrAttr<TFL_AFAttr, "TANH">:$fused_activation_function,
    ConfinedAttr<DefaultValuedOptionalAttr<F32Attr, "0.0f">, [TFL_FloatNonNegative]>:$cell_clip,
    ConfinedAttr<DefaultValuedOptionalAttr<F32Attr, "0.0f">, [TFL_FloatNonNegative]>:$proj_clip,
    // Since this op is the BASIC kernel only, constrain it.
    ConfinedAttr<

bool AllValuesAreZero(mlir::Value value) {
  if (!value) return false;
  DenseElementsAttr vals;
  if (!matchPattern(value, m_Constant(&vals))) return false;
  for (auto elem : vals.getValues<float>())
    if (elem != 0.0f) return false;
  return true;
}

bool IsF32Splat(Attribute input_splat) {
  if (!input_splat) return false;
  auto val = dyn_cast_or_null<DenseElementsAttr>(input_splat);
  if (val) {

            Public Key Algorithm: ED25519
                ED25519 Public-Key:
                pub:
                    36:29:c5:6c:0d:4f:14:6c:81:d0:ff:75:d3:6a:70:
                    5f:69:cd:0f:4d:66:d5:da:98:7e:82:49:89:a3:8a:
                    3c:fa
        X509v3 extensions:
            X509v3 Subject Key Identifier:
                09:3B:3A:9D:4A:29:D8:95:FF:68:BE:7B:43:54:72:E0:AD:A2:E3:AE

  result_values.reserve(output_size);

  for (int i = 0; i < output_size; ++i) {
    // Dot product with Kahan/Neumaier summation to minimize numeric errors.
    float sum = has_bias ? *bias_values_it : 0.0f;
    float compensation = 0.0f;
    for (int j = 0; j < input_size; ++j) {
      const float addend = input_values_it[j] * weights_row_it[j];
      const float new_sum = sum + addend;

				if err != nil {
					t.Fatalf("Obtaining succeeded job_pods_finished_total: %v", err)
				}
				if float64(tc.wantSucceededPodsMetric) != v {
					t.Errorf("Metric reports %.0f succeeded pods, want %d", v, tc.wantSucceededPodsMetric)
				}
				v, err = metricstestutil.GetCounterMetricValue(metrics.JobPodsFinished.WithLabelValues(completionMode, metrics.Failed))
				if err != nil {

  auto operands = adaptor.getOperands();
  assert(operands.size() == 1 && "leaky relu has one operand");

  // leaky_relu(x, alpha: 1) -> x
  if (getAlpha().convertToFloat() == 1.0f &&
      getOperand().getType() == getType())
    return getOperand();

  auto calculate = [&](FloatAttr arg) {
    APFloat val = arg.getValue();
    if (val.isNegative()) val = getAlpha() * val;