// CHECK: return %[[scale]], %[[zp]]
}

// CHECK-LABEL: quant_qparam_invalid
func.func @quant_qparam_invalid(%arg0: tensor<1x3x!quant.calibrated<f32<-1.0:1.0>>>) -> (!tfr.tensor, !tfr.tensor) {
  %0 = "tfr.cast"(%arg0) : (tensor<1x3x!quant.calibrated<f32<-1.0:1.0>>>) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%0) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  func.return %scale, %zp: !tfr.tensor, !tfr.tensor

import tensorflow  # pylint: disable=unused-import

from tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as stablehlo_quant_config_pb2
from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import custom_aggregator_op_wrapper
from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops

      // Quantize inputs of quantizable composite functions.
      for (OpOperand &input : op->getOpOperands()) {
        Type element_type = getElementTypeOrSelf(input.get().getType());
        // Non-float cases won't be calibrated.
        if (!element_type.isF32()) {
          continue;
        }

        // Skip when there is any already existing CustomAggregatorOp found.
        Operation *defining_op = input.get().getDefiningOp();

    %9, %9,
    %recurrent_stats, %cell_stats,
    %9, %9, %9, %9) {
      asymmetric_quantize_inputs = false,
      cell_clip = 1.000000e+01 : f32,
      effective_hidden_scale_intermediate = tensor<0x!quant.calibrated<f32<0.0:1.0>>>,
      fused_activation_function = "TANH",
      input_to_cell_intermediate = tensor<0xf32>,
      input_to_forget_intermediate = tensor<0xf32>,
      input_to_input_intermediate = tensor<0xf32>,

                                       elem_type.getIntOrFloatBitWidth());
  }

  // Intermediate tensors with calibration value (but not scale and zero points)
  // should return calibrated quantized type.
  if (is_intermediate && tensor.quantization != nullptr &&
      !IsQuantized(tensor)) {
    TF_ASSIGN_OR_RETURN(elem_type, GetCalibratedQuantizedType(tensor, builder));
  }

import itertools
import logging

import numpy as np

from tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as stablehlo_quant_config_pb2
from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_statistics_pb2 as calib_stats_pb2


_CalibrationMethod = (
    stablehlo_quant_config_pb2.CalibrationOptions.CalibrationMethod
)
_REGISTRY = {}

    op.erase();

    return success();
  }

 private:
  int num_bits;
  bool narrow_range;
  bool is_signed;
  bool legacy_float_scale;

  // Emits an op warning message if the calibrated range is larger than 10.0 and
  // the storage type is less than or equal to 8 bits.
  void TensorRangeSanityCheck(quantfork::StatisticsOp op, double& min,
                              double& max) const {

    // Types of the optional intermediate tensors, which exist for fully
    // quantized LSTM op and hold the ranges of the intermediate tensors.
    // The type for intermediate tensors are be quant.calibrated when imported
    // to only store calibrated min, max values. The proper quantization spec is
    // determined while going through quantization passes.
    OptionalAttr<TypeAttr>:$input_to_input_intermediate,

	tc.runTest(t)
}

// TestComputedToleranceAlgImplementation is a regression test which
// back-calculates a minimal percentage for downscaling based on a small percentage
// increase in pod utilization which is calibrated against the tolerance value.
func TestReplicaCalcComputedToleranceAlgImplementation(t *testing.T) {

	startPods := int32(10)
	// 150 mCPU per pod.
	totalUsedCPUOfAllPods := int64(startPods * 150)

        `is_dynamic_op=False` would be set.
    *   `converter.convert()` no longer returns a `tf.function`. Now the
        function must be accessed from the saved model.
    *   The `converter.calibrate()` method has been removed. To trigger
        calibration, a `calibration_input_fn` should be provided to
        `converter.convert()`.

*   Other: