.two-column-summary {
    display: grid;
    grid-template-columns: minmax(25%, max-content) minmax(25%, auto);
}
.three-column-summary {
    display: grid;
    grid-template-columns: minmax(15%, max-content) minmax(20%, max-content) minmax(20%, auto);
}
.three-column-release-summary {
    display: grid;
    grid-template-columns: minmax(40%, max-content) minmax(10%, max-content) minmax(40%, auto);
}
.four-column-summary {

	SIGIOINT    = syscall.Signal(0x10)
	SIGIOT      = syscall.Signal(0x6)
	SIGKAP      = syscall.Signal(0x3c)
	SIGKILL     = syscall.Signal(0x9)
	SIGLOST     = syscall.Signal(0x6)
	SIGMAX      = syscall.Signal(0xff)
	SIGMAX32    = syscall.Signal(0x3f)
	SIGMIGRATE  = syscall.Signal(0x23)
	SIGMSG      = syscall.Signal(0x1b)
	SIGPIPE     = syscall.Signal(0xd)
	SIGPOLL     = syscall.Signal(0x17)

  auto float_op = float_graph->operators()->Get(0);
  // Verify ArgMax input is quantized.
  ASSERT_THAT(float_graph->tensors()->Get(float_op->inputs()->Get(0))->type(),
              Eq(TensorType_FLOAT32));
  EXPECT_THAT(subgraph->tensors[op->inputs[0]].get()->type,
              Eq(TensorType_INT8));

  // Verify ArgMax input axis should still be the same type.

  Output s_x = Shape(scope, x);
  Output s_min = Shape(scope, min);
  Output s_max = Shape(scope, max);

  Output min_mask = Less(scope, x, min);
  Output max_mask = Greater(scope, x, max);

  auto r_min = internal::BroadcastGradientArgs(scope, s_x, s_min);
  auto r_max = internal::BroadcastGradientArgs(scope, s_x, s_max);

  Output grad = grad_inputs[0];
  Output zeros = ZerosLike(scope, grad);

			pvc:        validPersistentVolumeClaim("pvc-no-minmax-storage-request", getVolumeResourceRequirements(getStorageResourceList("100Gi"), getStorageResourceList(""))),
			limitRange: createLimitRange(api.LimitTypePersistentVolumeClaim, getStorageResourceList(""), getStorageResourceList(""), api.ResourceList{}, api.ResourceList{}, api.ResourceList{}),
		},
		{

	SIGIOINT    = syscall.Signal(0x10)
	SIGIOT      = syscall.Signal(0x6)
	SIGKAP      = syscall.Signal(0x3c)
	SIGKILL     = syscall.Signal(0x9)
	SIGLOST     = syscall.Signal(0x6)
	SIGMAX      = syscall.Signal(0x3f)
	SIGMAX32    = syscall.Signal(0x3f)
	SIGMIGRATE  = syscall.Signal(0x23)
	SIGMSG      = syscall.Signal(0x1b)
	SIGPIPE     = syscall.Signal(0xd)
	SIGPOLL     = syscall.Signal(0x17)

    {"RED",
     {"All", "Any", "Min", "Max", "Mean", "Prod", "Sum"}},
          // clang-format on
          {"PWRED",
           {"ArgMax", "ArgMin", "DiagPart", "Softmax",
            "SparseSoftmaxCrossEntropyWithLogits", "LogSoftmax"}},
          {"REDUCEWINDOW",
           {"ArgMax", "ArgMin", "DiagPart", "Softmax",
            "SparseSoftmaxCrossEntropyWithLogits", "LogSoftmax"}},

    (replaceWithValue $input),
    [(IsTailOfShape $input, $output),
     (IsTailOfShape $output, $input)]>;
}

// Remove (log-)softmax before arg-minmax as (log-)softmax is monotonic.
foreach ArgMinMaxOp = [TFL_ArgMinOp, TFL_ArgMaxOp] in {
  def RemoveSoftmaxOpBefore#ArgMinMaxOp : Pat<
    (ArgMinMaxOp (TFL_SoftmaxOp:$softmax $logits, TFL_FloatNonNegative:$beta),

  // operands from tflite op name.
  // Also, since the above code special-handles the `tfl.reshape` op and add an
  // additional input, we put these function block here.
  llvm::MinMax input_min_max = mlir::OperandNumbersMinMax(op_name);
  int input_max_num = input_min_max.Max;
  int op_input_num = op_state.operands.size();
  if (input_max_num != 0 && input_max_num > op_input_num) {