parameters: (sparse_id_column,
                       dimension,
                       combiner="mean",
                       initializer=None,
                       ckpt_to_load_from=None,
                       tensor_name_in_ckpt=None,
                       max_norm=None,
                       trainable=True)
return_statement: return _EmbeddingColumn(sparse_id_column, dimension, combiner, initializer,
                            ckpt_to_load_from, tensor_name_in_ckpt,
                            max_norm=max_norm, trainable=trainable)
docstring: Creates an `_EmbeddingColumn` for feeding sparse data into a DNN.
  
    Args:
      sparse_id_column: A `_SparseColumn` which is created by for example
        `sparse_column_with_*` or crossed_column functions. Note that `combiner`
        defined in `sparse_id_column` is ignored.
      dimension: An integer specifying dimension of the embedding.
      combiner: A string specifying how to reduce if there are multiple entries
        in a single row. Currently "mean", "sqrtn" and "sum" are supported, with
        "mean" the default. "sqrtn" often achieves good accuracy, in particular
        with bag-of-words columns. Each of this can be thought as example level
        normalizations on the column:
          * "sum": do not normalize
          * "mean": do l1 normalization
          * "sqrtn": do l2 normalization
        For more information: `tf.embedding_lookup_sparse`.
      initializer: A variable initializer function to be used in embedding
        variable initialization. If not specified, defaults to
        `tf.truncated_normal_initializer` with mean 0.0 and standard deviation
        1/sqrt(sparse_id_column.length).
      ckpt_to_load_from: (Optional). String representing checkpoint name/pattern
        to restore the column weights. Required if `tensor_name_in_ckpt` is not
        None.
      tensor_name_in_ckpt: (Optional). Name of the `Tensor` in the provided
        checkpoint from which to restore the column weights. Required if
        `ckpt_to_load_from` is not None.
      max_norm: (Optional). If not None, embedding values are l2-normalized to
        the value of max_norm.
      trainable: (Optional). Should the embedding be trainable. Default is True
  
    Returns:
      An `_EmbeddingColumn`.
docstring_summary: Creates an `_EmbeddingColumn` for feeding sparse data into a DNN.
docstring_tokens: Creates an _EmbeddingColumn for feeding sparse data into a DNN .
function: def embedding_column(sparse_id_column,
                       dimension,
                       combiner="mean",
                       initializer=None,
                       ckpt_to_load_from=None,
                       tensor_name_in_ckpt=None,
                       max_norm=None,
                       trainable=True):
    """Creates an `_EmbeddingColumn` for feeding sparse data into a DNN.
  
    Args:
      sparse_id_column: A `_SparseColumn` which is created by for example
        `sparse_column_with_*` or crossed_column functions. Note that `combiner`
        defined in `sparse_id_column` is ignored.
      dimension: An integer specifying dimension of the embedding.
      combiner: A string specifying how to reduce if there are multiple entries
        in a single row. Currently "mean", "sqrtn" and "sum" are supported, with
        "mean" the default. "sqrtn" often achieves good accuracy, in particular
        with bag-of-words columns. Each of this can be thought as example level
        normalizations on the column:
          * "sum": do not normalize
          * "mean": do l1 normalization
          * "sqrtn": do l2 normalization
        For more information: `tf.embedding_lookup_sparse`.
      initializer: A variable initializer function to be used in embedding
        variable initialization. If not specified, defaults to
        `tf.truncated_normal_initializer` with mean 0.0 and standard deviation
        1/sqrt(sparse_id_column.length).
      ckpt_to_load_from: (Optional). String representing checkpoint name/pattern
        to restore the column weights. Required if `tensor_name_in_ckpt` is not
        None.
      tensor_name_in_ckpt: (Optional). Name of the `Tensor` in the provided
        checkpoint from which to restore the column weights. Required if
        `ckpt_to_load_from` is not None.
      max_norm: (Optional). If not None, embedding values are l2-normalized to
        the value of max_norm.
      trainable: (Optional). Should the embedding be trainable. Default is True
  
    Returns:
      An `_EmbeddingColumn`.
    """
    return _EmbeddingColumn(sparse_id_column, dimension, combiner, initializer,
                            ckpt_to_load_from, tensor_name_in_ckpt,
                            max_norm=max_norm, trainable=trainable)
function_tokens: def embedding_column ( sparse_id_column , dimension , combiner = "mean" , initializer = None , ckpt_to_load_from = None , tensor_name_in_ckpt = None , max_norm = None , trainable = True ) : return _EmbeddingColumn ( sparse_id_column , dimension , combiner , initializer , ckpt_to_load_from , tensor_name_in_ckpt , max_norm = max_norm , trainable = trainable )