<< "output #" << i << " should be " << expected_output_type
             << " instead got " << output.getType();
  }
  return success();
}

mlir::LogicalResult SplitOp::verify() {
  SplitOp op = *this;
  int64_t num_splits = op.getNumSplits();
  if (op.getNumResults() != num_splits)
    return op.emitOpError("output count should match 'num_splits' attribute");

           << input_rank << ", " << input_rank << ")";
  }

  if (index < 0) index += input_rank;
  *dim_index = index;

  return success();
}

LogicalResult SplitOp::verify() {
  SplitOp op = *this;
  std::optional<int64_t> dim_index;
  if (failed(VerifySplitInputAndSplitDim(op, &dim_index))) return failure();
  if (!dim_index) return success();

  int64_t input_dim_size =

// TODO(antiagainst): consider lowering into TF ops so the pattern can be more
// applicable.
class ConvertSplitOp : public OpRewritePattern<TF::SplitOp> {
 public:
  using OpRewritePattern::OpRewritePattern;

  LogicalResult matchAndRewrite(TF::SplitOp op,
                                PatternRewriter &rewriter) const override {
    // We can only split inputs that have fully static shape.

//   tile_assignment_devices: 0
//   last_tile_dims: REPLICATED
// Serialized string:
//   "\08\03\1A\06\01\01\01\01\01\01\22\01\00B\01\00"

// Test that an input sharding with last_tile_dims REPLICATED won't generate SplitOp.
//CHECK-NOT: tf.Split