QuantizationUnits& quantizable_ops) const {
    bool quantized = false;

    for (auto& quant_op : quantizable_ops) {
      if (quant_specs_.inference_type == tensorflow::DT_QINT8) {
        quantized |= quantizeOpAsInt8(rewriter, op, quant_op);
      }
    }
    return quantized;
  }

 protected:
  QuantizationSpecs quant_specs_;
  OpSet op_set_;

          GetAsVector(expected_tensor->shape()));
}

// Finds the match of the quantized tensor from the possible tensors. Each
// possible tensors can be used only once. It checks shape and name if the
// tensor is quantized and also checks buffer contents and tensor type if not
// quantized. For the quantized case, tensor type and quantizaction params are
// expected to be checked in the test body with the match.

  // in the quantized model. Both models are required to be able to dump
  // both quantized and unquantized tensors and compare them offline.
  if (quantization_options.has_debugger_config() &&
      quantization_options.debugger_config().debugger_type() ==
          DebuggerConfig::DEBUGGER_TYPE_WHOLE_MODEL) {
    TF_ASSIGN_OR_RETURN(
        ExportedModel debugging_exported_model,

  string checkpoint_dir = 5;

  // Function name -> function alias mapping. This associates the quantized
  // functions to the original functions' aliases. This information will be used
  // to populate `MetaInfoDef`s `function_aliases` when the quantized model is
  // exported to the saved model. This field is usually only populated for the
  // TF2 models.
  map<string, string> function_aliases = 6;

// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:     -stablehlo-quantize-composite-functions | FileCheck --check-prefix=CHECK %s

// Test that per-tensor weight-only quantized dot_general op is produced when
// empty `weight_only_ptq` is provided.

module attributes {tf_saved_model.semantics} {
  func.func private @quantize_dot_general_per_tensor(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {

    if bias_fn:
      self.assertTrue(re.search('stablehlo.add.*xi32>', module_str))
    # Consider if there is a way to check if activation fusion is properly
    # done in MLIR level.
    # Tests that the quantized graph outputs similar values. The rtol and atol
    # values are arbitrary.
    self.assertAllClose(new_outputs, expected_outputs, rtol=0.3, atol=0.2)

    # Due to other meta data, the compression is not exactly 1/4.

        op_set=target_opset,
    )

    if target_opset != quant_opts_pb2.XLA:
      # Uniform quantized opset is not supported for weight-only
      with self.assertRaisesRegex(
          ValueError, 'TF/Uniform quantized opset does not support weight-only.'
      ):
        converted_model = quantize_model.quantize(
            input_saved_model_path,
            output_directory,
            quantization_options,

// Decompose ops.
std::unique_ptr<OperationPass<func::FuncOp>> CreateDecomposeTFOpsPass(
    std::optional<ModuleOp> tfr_module = std::nullopt);

// Rewrites quantized operands and results with their storage types.
// This pass should be run at module level after decomposition, if there are
// quantized operands or results.
std::unique_ptr<OperationPass<ModuleOp>> CreateRewriteQuantizedIOPass();

// Raise to TF ops.

              Eq(TensorType_INT8));

  // Verify FC bias should be int32 quantized.
  ASSERT_THAT(float_graph->tensors()->Get(float_op->inputs()->Get(2))->type(),
              Eq(TensorType_FLOAT32));
  EXPECT_THAT(subgraph->tensors[op->inputs[2]].get()->type,
              Eq(TensorType_INT32));

  // The output tensor of FC should be int8 quantized.
  ASSERT_THAT(float_graph->tensors()->Get(float_op->outputs()->Get(0))->type(),

      }
    }
    elem_type = mlir::TF::VariantType::get(tensor_types, builder.getContext());
  }
  if (IsQuantized(tensor) && !get_storage) {
    TF_ASSIGN_OR_RETURN(elem_type,
                        GetQuantizedType(tensor, builder, is_constant));
  } else if (IsQuantized(tensor) && get_storage) {
    // If the type is quantized we strip the signedness from the storage type.
    elem_type = mlir::IntegerType::get(elem_type.getContext(),