code: def _build_deep_columns(
      user_ids,
      item_ids,
      user_dim,
      item_dim,
      item_feat_col=None,
      item_feat_shape=1,
  ):
      """Build deep feature columns
  
      Args:
          user_ids (tf.feature_column.categorical_column_with_vocabulary_list): User ids.
          item_ids (tf.feature_column.categorical_column_with_vocabulary_list): Item ids.
          user_dim (int): User embedding dimension.
          item_dim (int): Item embedding dimension.
          item_feat_col (str): Item feature column name.
          item_feat_shape (int or an iterable of integers): Item feature array shape.
  
      Returns:
          list: Deep feature columns.
      """
      deep_columns = [
          # User embedding
          tf.feature_column.embedding_column(
              categorical_column=user_ids,
              dimension=user_dim,
              max_norm=user_dim**0.5,
          ),
          # Item embedding
          tf.feature_column.embedding_column(
              categorical_column=item_ids,
              dimension=item_dim,
              max_norm=item_dim**0.5,
          ),
      ]
      # Item feature
      if item_feat_col is not None:
          deep_columns.append(
              tf.feature_column.numeric_column(
                  item_feat_col, shape=item_feat_shape, dtype=tf.float32
              )
          )
      return deep_columns
  
imports: import tensorflow
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL
  from recommenders.utils.tf_utils import MODEL_DIR
label_desc: Use image node_id 3 for calling a global function with example usage: _build_deep_columns(user_ids, item_ids, user_dim, item_dim, item_feat_col, item_feat_shape) and returns: deep_columns