return failure();
  }

  // When creating the new const op, use a fused location that combines the
  // original const and the qbarrier that led to the quantization.
  auto fusedLoc = rewriter.getFusedLoc(
      {qbarrier.getArg().getDefiningOp()->getLoc(), qbarrier.getLoc()});
  auto newConstOp = rewriter.create<arith::ConstantOp>(
      fusedLoc, newConstValueType, cast<TypedAttr>(newConstValue));

    SmallVector<float> transposed_values =
        transposer.TransposeValues(original_values);

    // Create a new constant op with the transposed values.
    const Location combined_loc =
        rewriter.getFusedLoc({const_op.getLoc(), op.getLoc()});
    auto new_value_type =
        RankedTensorType::getChecked(combined_loc, transposer.GetTargetShape(),
                                     /*elementType=*/rewriter.getF32Type());

      result_type = activation->getResultTypes().front();
    } else {
      result_type = bias_add.getResult().getType();
    }

    auto fused_loc = rewriter.getFusedLoc(locations);

    // The fused contraction has the same operands as the original contraction
    // with `bias` from the BiasAddOp appended.
    SmallVector<Value, 4> operands(contraction.operand_begin(),

          });
    }

    is_training =
        fused_batch_norm_op->getAttrOfType<::mlir::BoolAttr>("is_training");
    auto odsLoc = rewriter.getFusedLoc({fused_batch_norm->getLoc()});

    // We need to make sure input and output shapes are compatible.
    int64_t last_dim = ShapedType::kDynamic;
    {