def ConvertDotGeneralOp : NativeCodeCall<"ConvertDotGeneralOp($_builder, "
                                               "$0.getDefiningOp())">;
def LegalizeDotGeneral: Pat<(MHLO_DotGeneralOp:$old_value
               $lhs,
               $rhs,
               $dot_dimension_numbers, $precision_config),

// Concat ) are inserted for shape inference purposes.
// 4) All the DotOp are converted to DotGeneral during the optimization pass
// (ConvertDotOp).
Value ConvertDotGeneralOp(PatternRewriter& rewriter, Operation* old_op);
}  // namespace odml
}  // namespace mlir

def : Pat<(MHLO_DotOp:$old_value StaticShapeTensorOf<[TF_ElementType]>:$lhs,
               StaticShapeTensorOf<[TF_ElementType]>:$rhs, $precision_config),
          (ConvertDotOp $old_value)>;

def ConvertDotGeneralOp : NativeCodeCall<"ConvertDotGeneralOp($_builder, "
                                               "$0.getDefiningOp())">;
def : Pat<(MHLO_DotGeneralOp:$old_value
               RankedTensorOf<[TF_ElementType]>:$lhs,

      loc, result_type, matmul.getResult(), result_shape);
  return reshaped.getResult();
}

// Converts mhlo.dot_general to tfl.BatchMatMul. Reshape and Transpose ops will
// be inserted when necessary. See ConvertDotGeneralOp for additional notes.
Value ConvertDotOp(PatternRewriter& rewriter, Operation* old_op) {
  auto dot_op = cast<mhlo::DotOp>(old_op);
  auto lhs_rank = mlir::cast<ShapedType>(dot_op.getLhs().getType()).getRank();

}

// Converts mhlo.dot to tf.BatchMatMul. Reshape or Transpose ops will also be
// inserted to convert to well-formed matrix multiply.
Value ConvertDotGeneralOp(PatternRewriter& rewriter, Operation* old_op) {
  auto dot_general_op = cast<mhlo::DotGeneralOp>(old_op);
  return ConvertDot(
      rewriter, dot_general_op.getLhs(), dot_general_op.getRhs(),