quant_params_.quant_spec.nodes_blocklist;
    CustomMap custom_map = quant_params_.quant_spec.custom_map;

    // 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.

// After this optimization:
//   %cst = arith.constant dense<1.0> : tensor<f32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<f32>)
// This pattern can enable more fusing opportunities when the binary op is
// following conv ops.
template <typename BinaryOpType>
struct ScalarizeSplatConstantForBroadcastableOps
    : public OpRewritePattern<BinaryOpType> {

        if activation_fn is not None:
          # The accuracy is not good when having FusedBatchNorm without
          # activation in this test.
          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
            )
          out = activation_fn(out)

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*   `tf.nn.embedding_lookup_sparse`

    *   Optimized this function for some cases by fusing internal operations.

*   `tf.saved_model.SaveOptions`