return "quant-prepare-lifting";
  }

  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Apply graph optimizations such as fusing and constant folding to "
           "prepare lifting.";
  }

  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect, arith::ArithDialect>();
  }

        mlir::TFL::CreatePostQuantizePass(emit_quant_adaptor_ops));
  }
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreateOptimizeOpOrderPass());
  // Add optimization pass after quantization for additional fusing
  // opportunities.

  if (!pass_config.unfold_batch_matmul) {
    // Enable an optimization pass that transforms FC to BatchMatmul only when
    // `unfold_batch_matmul=false`.

    // Safeguard check to ensure that there is at least one quantizable op.
    if (failed(candidate_ops) || candidate_ops->empty()) return failure();

    // Rewrite the floating-point ops to the quantized version, by fusing
    // preceding dequantize ops and succeding quantize ops.
    for (Operation* candidate_op : *candidate_ops) {
      // If it is requantize op, we shouldn't rewrite this op.

  </mime-type>
  <mime-type type="application/illustrator+ps">
    <acronym>AI</acronym>
    <_comment>Adobe Illustrator Artwork -- the older postscript based AI files</_comment>
    <!-- we can do more specific versions by focusing on the integer in the regex below -->
    <tika:link>http://justsolve.archiveteam.org/wiki/Adobe_Illustrator_Artwork</tika:link>
    <magic priority="60">

std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsPass(
    const tensorflow::quantization::QuantizationOptions& quant_options);

// Apply graph optimizations such as fusing and constant folding to prepare
// lifting.
std::unique_ptr<OperationPass<func::FuncOp>> CreatePrepareLiftingPass(
    tensorflow::quantization::OpSet target_opset);

// RUN: tf-tfrt-opt -verify-diagnostics -split-input-file -tfrt-fuse-tpu-compile-and-execute-ops -canonicalize %s | FileCheck %s --dump-input=fail --dump-input-filter=all

module attributes {tf_saved_model.semantics} {

// Test fusing _TPUCompileMlirOp and TPUExecuteOp into TPUCompileMlirAndExecuteOp.

// CHECK-LABEL: func private @test_fuse_tpu_ops
func.func private @test_fuse_tpu_ops(%arg0: tensor<*xi32>, %arg1: tensor<*x!tf_type.resource>) -> tensor<*xi32> {

            data_format='NHWC',
            name='sample/conv',
        )
        if bias_fn is not None:
          out = nn_ops.bias_add(out, self.bias)
        if has_batch_norm:
          # Fusing is supported for non-training case.
          out, _, _, _, _, _ = nn_ops.fused_batch_norm_v3(
              out, scale, offset, mean, variance, is_training=False
          )
        if activation_fn is not None:

    auto users = op.getResult().getUsers();
    quantizing_ops.append(users.begin(), users.end());

    bool changed = false;
    // Rewrite the floating-point ops to the quantized version, by fusing
    // preceding dequantize ops and succeding quantize ops.
    for (Operation* quantizing_op : quantizing_ops) {
      // If it is requantize op, we shouldn't rewrite this op.

            padding='SAME',
            data_format='NHWC',
        )
        if has_bias:
          out = nn_ops.bias_add(out, self.bias)
        if has_batch_norm:
          # Fusing is supported for non-training case.
          out, _, _, _, _, _ = nn_ops.fused_batch_norm_v3(
              out, scale, offset, mean, variance, is_training=False
          )
        if activation_fn is not None:

      // Found an operand that isn't scheduled yet, return true.
      return true;
    }
  }
  return false;
}

// Sorts the operations in the provided range to enforce dominance.
// This is useful after fusing / reorganizing Operations in a block and later
// needing to readjust the ordering to ensure dominance.
LogicalResult SortTopologically(Block::iterator begin, Block::iterator end) {
  Block* block = begin->getBlock();