code: def reassemble_sequence_feats(
      shape,
      row_sequence_feats,
      col_sequence_feats,
      row_scatter_vals,
      col_scatter_vals,
      include_row_col_summaries,
      down_project_feats,
  ):
      if include_row_col_summaries:
          feat_dim = 768 * 4
          summaries = [
              scatter_feats(
                  shape,
                  get_summary_values(
                      col_sequence_feats, col_scatter_vals, shape[1]
                  ),
                  col_scatter_vals,
              ),
              scatter_feats(
                  shape,
                  get_summary_values(
                      row_sequence_feats, row_scatter_vals, shape[1]
                  ),
                  row_scatter_vals,
              ),
          ]
      else:
          feat_dim = 768 * 2
          summaries = []
  
      feats = tf.concat(
          [
              scatter_feats(
                  shape, col_sequence_feats, col_scatter_vals
              ),
              scatter_feats(
                  shape, row_sequence_feats, row_scatter_vals
              ),
              *summaries,
          ],
          -1,
      )
      feats.set_shape([None, None, feat_dim])
      if down_project_feats:
          feats = tf.compat.v1.layers.dense(feats, 768, activation=gelu)
  
      return feats
  
file_path: finetune/finetune/base_models/bert/table_utils.py
imports: import tensorflow
  from finetune.base_models.bert.modeling import gelu
label_desc: Use image node_id 10 for calling a global function with example usage: reassemble_sequence_feats(shape, row_sequence_feats, col_sequence_feats, row_scatter_vals, col_scatter_vals, include_row_col_summaries, down_project_feats) and returns: feats