//       input_shape[M+1], ..., input_shape[N-1]]
    SmallVector<int64_t> permutation(reshaped_shape.size());
    permutation[0] = block_rank;
    for (int i = 0; i < block_rank; ++i) {
      permutation[1 + 2 * i] = block_rank + 1 + i;
      permutation[1 + 2 * i + 1] = i;
    }
    std::iota(permutation.begin() + 1 + block_rank * 2, permutation.end(),
              1 + block_rank * 2);

  (TFL_TransposeOp
    (TFL_TransposeOp:$transpose_out1 $input, (Arith_ConstantOp:$permutation1 $p1)),
    (Arith_ConstantOp:$permutation2 $p2)),
  (TFL_TransposeOp $input,
    (Arith_ConstantOp (RemapPermutation $permutation1, $permutation2))),
  [(HasOneUse $transpose_out1)]>;

// Convert expand_dims to reshape if possible.
def ConvertExpandDimsToReshape : Pat<
  (TFL_ExpandDimsOp:$expand_dims_op $input, $dim),

  // method that it is a 4-D RankedTensorType.
  Value legalizeFilter(PatternRewriter &rewriter, Location loc,
                       Value filter) const {
    // Create a constant op for HWIO to OHWI transpose permutation.
    SmallVector<int, 4> perm = {3, 0, 1, 2};
    auto perm_type = tensorflow::GetTypeFromTFTensorShape(
        {static_cast<int>(perm.size())}, rewriter.getIntegerType(32));
    auto perm_attr =