code: def table_cross_row_col_mixing_fn(
      row_feats,
      col_feats,
      *,
      row_gather,
      col_gather,
      output_shape,
      row_col_values,
  ):
      bos_var = tf.compat.v1.get_variable(
          "bos",
          shape=[768],
          dtype=tf.float32,
          initializer=tf.compat.v1.truncated_normal_initializer(),
          trainable=True,
      )
      eos_var = tf.compat.v1.get_variable(
          "eos",
          shape=[768],
          dtype=tf.float32,
          initializer=tf.compat.v1.truncated_normal_initializer(),
          trainable=True,
      )
  
      col_feats_orig_shape = scatter_feats(
          output_shape, col_feats, row_col_values["col"]["scatter_vals"]
      )
      row_feats_orig_shape = scatter_feats(
          output_shape, row_feats, row_col_values["row"]["scatter_vals"]
      )
  
      # Scatter col feats into rows arrangement
      col_feats_reshaped = gather_col_vals(
          col_feats_orig_shape,
          row_gather,
          bos_pad=bos_var,
          eos_pad=eos_var,
          pad_val=1234,
      )["values"]
  
      # Scatter row feats into cols arrangement.
      row_feats_reshaped = gather_col_vals(
          row_feats_orig_shape,
          col_gather,
          bos_pad=bos_var,
          eos_pad=eos_var,
          pad_val=1234,
      )["values"]
  
      return (
          adaptor_block(col_feats_reshaped, 64) + row_feats,
          adaptor_block(row_feats_reshaped, 64) + col_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 12 for calling a global function with example usage: table_cross_row_col_mixing_fn(row_feats, col_feats) and returns: unknown,  unknown