llvm::SmallVector<int64_t, 4> high_padding(rank, 0);
      llvm::SmallVector<int64_t, 4> interior_padding(rank, 0);
      low_padding[axis] = 1;
      high_padding[axis] = -1;
      result = rewriter.create<PadOp>(
          op.getLoc(), result, init, GetI64ElementsAttr(low_padding, &rewriter),
          GetI64ElementsAttr(high_padding, &rewriter),

          axis=i,
          num_split=2)
      _, right_padding = tf.raw_ops.SplitV(
          value=rarray,
          size_splits=[-1, right_padding_size],
          axis=i,
          num_split=2)
    else:
      _, left_padding = tf.raw_ops.SplitV(
          value=rarray,
          size_splits=[-1, left_padding_size],
          axis=i,
          num_split=2)
      right_padding, _ = tf.raw_ops.SplitV(
          value=rarray,

def _tfr_tf_ops_complex(lhs, rhs):
  left_padding, _ = array_ops.SplitV(
      value=lhs, size_splits=[rhs, -1], axis=0, num_split=2)
  _, right_padding = array_ops.SplitV(
      value=lhs, size_splits=[rhs, rhs], axis=1, num_split=2)
  return [left_padding, right_padding]


@composite.Composite('TestIdentityOp')
def _tfr_tf_ops_tensor(x):
  return array_ops.Identity(x)

      int64_t left_padding = padding[2 * dim];
      int64_t right_padding = padding[2 * dim + 1];
      if (dim == cumulative_axis) {
        if (left_padding != padding_value) return failure();
      } else {
        if (left_padding != 0) return failure();
      }
      if (right_padding != 0) return failure();
    }

    auto axis = rewriter.create<TF::ConstOp>(