In a real life application you would need to hash the password and never save them in plaintext.

    For more details, go back to the Security section in the tutorial.

    Here we are focusing only on the tools and mechanics of databases.

!!! tip

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// Fusing:  %[[add3:[0-9].*]] = tfl.add %[[add2]], %[[relu]] {fused_activation_function = "RELU6"} : tensor<1xf32>
// Fusing:  return

// NoFusing-LABEL: FusingaddRelu
// NoFusing:  %[[add:[0-9].*]] = tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xf32>
// NoFusing:  %[[add1:[0-9].*]] = tfl.add %arg0, %[[add]] {fused_activation_function = "RELU"} : tensor<1xf32>

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// TODO(b/158266710): Support CPU MKL configurations.

#define GEN_PASS_DEF_FUSEDKERNELMATCHERPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

// Optimizes TF computations by fusing subgraphs/nodes onto more efficient
// implementations to decrease the number of operations needed to perform a
// computation.
struct FusedKernelMatcherPass
    : public impl::FusedKernelMatcherPassBase<FusedKernelMatcherPass> {

    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.

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);

            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: