EXPECT_THAT(output->quantization->zero_point,
              Eq(input1->quantization->zero_point));
  EXPECT_THAT(output->quantization->scale, Eq(input2->quantization->scale));
  EXPECT_THAT(output->quantization->zero_point,
              Eq(input2->quantization->zero_point));
}

INSTANTIATE_TEST_SUITE_P(MinimumMaximumTestInst, QuantizeMinimumMaximumTest,

from tensorflow.compiler.mlir.quantization.common.python import testing
from tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as qc
from tensorflow.compiler.mlir.quantization.stablehlo.python import quantization
from tensorflow.compiler.mlir.quantization.stablehlo.python.integration_test import quantize_model_test_base

        "//tensorflow/compiler/mlir/lite/quantization/ir:QuantOps",
        "//tensorflow/compiler/mlir/lite/quantization/lite:tfl_to_std",
        "//tensorflow/compiler/mlir/lite/schema:schema_fbs",
        "//tensorflow/compiler/mlir/quantization/common:uniform_quantized_types",
        "//tensorflow/compiler/mlir/quantization/common/ir:QuantOps",
        "//tensorflow/compiler/mlir/quantization/common/quantization_lib",

          clEnumValN(tensorflow::quantization::QuantizationMethod::
                         METHOD_DYNAMIC_RANGE_INT8,
                     "drq", "Post-training dynamic-range quantizaiton"),
          clEnumValN(tensorflow::quantization::QuantizationMethod::
                         METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8,
                     "weight_only", "Post-training weight-only quantization"))};

#include "stablehlo/dialect/StablehloOps.h"  // from @stablehlo
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"
#include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.h"
#include "tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.h"

#define DEBUG_TYPE "stablehlo-compose-uniform-quantized-type"

namespace mlir {
namespace odml {
namespace {

    asset_file = os.path.join(asset_dir, 'vocab_file.txt')
    content = '\n'.join(['static', 'range', 'quantization'])
    file_io.write_string_to_file(filename=asset_file, file_content=content)

    # The resulting table looks like:
    # "static" -> 0
    # "range" -> 1
    # "quantization" -> 2
    # default -> -1
    init = lookup_ops.TextFileInitializer(
        filename=asset_file,

  if (!tensor.quantization || tfl::IsQuantized(tensor)) return nullptr;
  // If the result isn't float and unquantizable, the min/max is ignored.
  if (!res.getType()
           .cast<mlir::ShapedType>()
           .getElementType()
           .isa<mlir::FloatType>()) {
    return nullptr;
  }
  auto mins = tensor.quantization->min;
  auto maxs = tensor.quantization->max;

        "//tensorflow/compiler/mlir/lite/quantization/lite:quantize_model",
        "//tensorflow/compiler/mlir/quantization/common/quantization_lib:quantization_config",
        "//tensorflow/compiler/mlir/lite/sparsity:sparsify_model",
        "//tensorflow/compiler/mlir/quantization/stablehlo/python:pywrap_quantization_lib_impl",

        module = "",
        platforms = [],
        version = 5 : i64,
        _entry_function = @composite_conv_fn,
        _original_entry_function = "composite_conv_fn",
        // Per-channel quantization at dimension 3 for input index 1.
        _quantization_method = "static_range_ptq {input_quantized_types {key: 1, value {dimension_specs {dimension: 3}}}}",
        _stablehlo_module_attrs = {},

// RUN: tf-opt %s -tfl-prepare-quantize="quantize-allowlist=quantize_float_placeholder_only,not_reset_input" | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize="disable-set-input-nodes-quantization-params=true" | FileCheck --check-prefix=MixedPrecision %s
// RUN: tf-opt %s -tfl-prepare-quantize="is-qdq-conversion=true" | FileCheck --check-prefix=QDQ %s

// CHECK-LABEL: main
// Uses `main` function to match the default target function of QuantSpecs and