func.return %0 : tensor<*xf32>
}

// -----

func.func @testUniformQuantize(%arg0: tensor<*xf32>, %scales: tensor<2xf32>, %zps: tensor<i32>) -> tensor<*x!tf_type.qint8> {
  // expected-error @below {{'tf.UniformQuantize' op quantization_axis is -1, scales must have 0 rank.}}
   %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

          mlir::dyn_cast<mlir::quant::UniformQuantizedType>(element_type)) {
    std::vector<float> scales = {static_cast<float>(qtype.getScale())};
    std::vector<int64_t> zero_points = {qtype.getZeroPoint()};
    q_params = tflite::CreateQuantizationParameters(
        builder_, /*min=*/0, /*max=*/0, builder_.CreateVector<float>(scales),
        builder_.CreateVector<int64_t>(zero_points));

}

namespace {

template <typename UniformQuantizedOp>
LogicalResult VerifyScalesAndZeroPoints(UniformQuantizedOp op, Value scales,
                                        Value zero_points,
                                        int32_t quantization_axis) {
  ShapedType scales_type = scales.getType().cast<ShapedType>();
  ShapedType zero_points_type = zero_points.getType().cast<ShapedType>();

  if (quantization_axis == -1) {

// this bias a dummy bias (note that bias fusion is not considered for this
// transformation). The quantization scale for the bias is input scale *
// filter scale. `filter_const_op` is used to retrieve the filter scales and
// the size of the bias constant.
TFL::QConstOp CreateTflConstOpForDummyBias(
    const Location loc, const double input_scale, TFL::QConstOp filter_const_op,

    auto result_type = getOutput().getType();
    // central_value = min_value / 2 + (max_value - 1) / 2 + 1
    // zero_point = central_value
    // scale = 1. / (central_value - min_value)
    return quant::GetFixedOutputRange(is_signed, bit_width, result_type,
        /*scale=*/1.0 / (1<<(bit_width-1)), /*zero_point=*/0);
  }
  }];
}

def TFL_LeakyReluOp: TFL_Op<"leaky_relu", [
    SameOperandsAndResultShape,

			scaleDownStabilizationWindowSeconds: 3 * 60,
		},
		{
			// we can reuse only the first recommendation element
			// as the scale up delay = 150 (set in test), scale down delay = 300 (by default)
			// hence, only the first recommendation is obsolete for both scale up and scale down
			name: "scale up stabilization, reuse one of obsolete recommendation element",
			key:  "",
			recommendations: []timestampedRecommendation{

No <a href="#Run_time_panics">run-time panic</a> occurs in this case.
</p>


<h3 id="Calls">Calls</h3>

<p>
Given an expression <code>f</code> of function type
<code>F</code>,
</p>

<pre>
f(a1, a2, … an)
</pre>

<p>
calls <code>f</code> with arguments <code>a1, a2, … an</code>.
Except for one special case, arguments must be single-valued expressions

      "maxDataPoints": 100,
      "options": {
        "colorMode": "none",
        "graphMode": "area",
        "justifyMode": "auto",
        "orientation": "horizontal",
        "reduceOptions": {
          "calcs": [
            "lastNotNull"
          ],
          "fields": "",
          "values": false
        },
        "textMode": "auto"
      },
      "pluginVersion": "10.1.5",
      "targets": [

        },

	{ACCMN, C_COND, C_ZREG, C_ZREG, C_VCON, C_NONE, 19, 4, 0, 0, 0},
	{ACCMN, C_COND, C_ZREG, C_VCON, C_VCON, C_NONE, 19, 4, 0, 0, 0},
	{AFCCMPS, C_COND, C_FREG, C_FREG, C_VCON, C_NONE, 57, 4, 0, 0, 0},

	/* scaled 12-bit unsigned displacement store */
	{AMOVB, C_ZREG, C_NONE, C_NONE, C_UAUTO4K, C_NONE, 20, 4, REGSP, 0, 0},
	{AMOVB, C_ZREG, C_NONE, C_NONE, C_UOREG4K, C_NONE, 20, 4, 0, 0, 0},