%1 = "quantfork.dcast"(%0) : (tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0011764706057660721:-43>>) -> tensor<1x3x4x3xf32>
  %q_w = "quantfork.qcast"(%cst) : (tensor<2x3x3x2xf32>) -> tensor<2x3x3x2x!quant.uniform<i8:f32, 0.0125:-24>>
  %dq_w = "quantfork.dcast"(%q_w) : (tensor<2x3x3x2x!quant.uniform<i8:f32, 0.0125:-24>>) -> tensor<2x3x3x2xf32>

  return mlir::cast<TensorType>(value.getType()).getElementType();
}

// Returns true iff `type` is a uniform quantized type whose storage type is
// 8-bit integer and expressed type is f32.
bool IsI8F32UniformQuantizedType(Type type);

// Returns true iff `type` is a uniform quantized per-axis (per-channel) type
// whose storage type is 8-bit integer and expressed type is f32.

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<f32>) -> tensor<f32> {
    %0 = "stablehlo.uniform_quantize"(%arg0) : (tensor<f32>) -> tensor<!quant.uniform<ui8:f32, 34.0:16>>
    %1 = "stablehlo.uniform_dequantize"(%0) : (tensor<!quant.uniform<ui8:f32, 34.0:16>>) -> tensor<f32>
    func.return %1 : tensor<f32>
  }
}

// CHECK-LABEL: HloModule main
// CHECK:       ENTRY %main.{{[0-9]+}} ([[ARG0:.*]]: f32[]) -> (f32[]) {

    FileTypes(String fileExtension, String identifier) {
        this.fileExtension = fileExtension;
        this.identifier = identifier;
    }


    /**
     * Map of file extension to Apple UTI (Uniform Type Identifier).
     */
    public static final ImmutableMap<String, String> FILE_EXTENSION_TO_UTI;

    static {
        ImmutableMap.Builder<String, String> builder = ImmutableMap.builder();

namespace mlir::quant::stablehlo {

// Creates an instance of the ConvertTFQuantOpsToMHLOPass pass, which will
// convert TF uniform quantized ops to the corresponding quantized MHLO ops.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateConvertTFQuantOpsToMHLOPass();

// TODO(b/288094093): Migrate uniform quantization legalization in a separate
// pass.
void PopulateLegalizeTfQuantizationPatterns(MLIRContext *context,

std::unique_ptr<Pass> createOptimizePass();

// Creates a pass that finds quantization patterns and compose them to uniform
// quantized types.
std::unique_ptr<OperationPass<ModuleOp>>
CreateComposeUniformQuantizedTypePass();

// Creates a pass that finds stablehlo ops that accept or produce uniform
// quantized typed tensors and converts them to equivalent ops in the TFLite
// dialect.
std::unique_ptr<OperationPass<func::FuncOp>>

// distribution). This makes it easier to work with as it's being
// updated.
//
// It is represented as the sum of scaled uniform distribution
// functions and Dirac delta functions (which are treated as
// degenerate uniform distributions).
type mud struct {
	sorted, unsorted []edge

	// trackMass is the inverse cumulative sum to track as the
	// distribution is updated.
	trackMass float64

    def data_gen():
      for _ in range(255):
        yield {
            'x': ops.convert_to_tensor(
                np.random.uniform(size=(10)).astype('f4')
            ),
            'y': ops.convert_to_tensor(
                np.random.uniform(size=(10)).astype('f4')
            ),
        }

    root = ModelWithAdd()

    temp_path = self.create_tempdir().full_path
    saved_model_save.save(

  }];
  let options = [
    Option<"unpack_quantized_types_", "unpack-quantized-types", "bool",
      /*default=*/"true", "Unpacks ops with uniform quantized types into "
      "operations without uniform quantized types (mostly i8 or i32).">
  ];
  let dependentDialects = [
    "mlir::stablehlo::StablehloDialect", "mlir::TF::TensorFlowDialect",

  %0 = arith.constant dense<[[1.0], [2.0]]> : tensor<2x1xf32>
  %1 = "tfl.quantize"(%0) {qtype = tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>} : (tensor<2x1xf32>) -> tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>
  %2 = "tfl.dequantize"(%1) : (tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>) -> tensor<2x1xf32>