1 files changed, 43 insertions, 44 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.framework.md b/tensorflow/g3doc/api_docs/python/contrib.framework.md
index 4c23455597..df4df30d19 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.framework.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.framework.md
@@ -227,50 +227,49 @@ adds them via `tf.add_n`.
 
 - - -
 
-### `tf.contrib.framework.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner='mean', default_id=None, name=None, partition_strategy='div')` {#safe_embedding_lookup_sparse}
-
-Lookup embedding results, accounting for invalid IDs and empty features.
-
-The partitioned embedding in `embedding_weights` must all be the same shape
-except for the first dimension. The first dimension is allowed to vary as the
-vocabulary size is not necessarily a multiple of `P`.
-
-Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
-with non-positive weight. For an entry with no features, the embedding vector
-for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
-
-The ids and weights may be multi-dimensional. Embeddings are always aggregated
-along the last dimension.
-
-##### Args:
-
-
-*  <b>`embedding_weights`</b>: A list of `P` float tensors or values representing
-      partitioned embedding tensors.  The total unpartitioned shape should be
-      `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
-      `e_1, ..., e_m` are the embedding dimensions.
-*  <b>`sparse_ids`</b>: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
-      ids. `d_0` is typically batch size.
-*  <b>`sparse_weights`</b>: `SparseTensor` of same shape as `sparse_ids`, containing
-      float weights corresponding to `sparse_ids`, or `None` if all weights
-      are be assumed to be 1.0.
-*  <b>`combiner`</b>: A string specifying how to combine embedding results for each
-      entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
-      the default.
-*  <b>`default_id`</b>: The id to use for an entry with no features.
-*  <b>`name`</b>: A name for this operation (optional).
-*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy.
-      Currently `"div"` and `"mod"` are supported. Default is `"div"`.
-
-
-##### Returns:
-
-  Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `embedding_weights` is empty.
+### `tf.contrib.framework.safe_embedding_lookup_sparse(*args, **kwargs)` {#safe_embedding_lookup_sparse}
+
+Lookup embedding results, accounting for invalid IDs and empty features. (deprecated)
+
+THIS FUNCTION IS DEPRECATED. It will be removed after 2016-09-01.
+Instructions for updating:
+Please use tf.contrib.layers.safe_embedding_lookup_sparse.
+
+  The partitioned embedding in `embedding_weights` must all be the same shape
+  except for the first dimension. The first dimension is allowed to vary as the
+  vocabulary size is not necessarily a multiple of `P`.
+
+  Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
+  with non-positive weight. For an entry with no features, the embedding vector
+  for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
+
+  The ids and weights may be multi-dimensional. Embeddings are always aggregated
+  along the last dimension.
+
+  Args:
+    embedding_weights:  A list of `P` float tensors or values representing
+        partitioned embedding tensors.  The total unpartitioned shape should be
+        `[e_0, e_1, ..., e_m]`, where `e_0` represents the vocab size and
+        `e_1, ..., e_m` are the embedding dimensions.
+    sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
+        ids. `d_0` is typically batch size.
+    sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
+        float weights corresponding to `sparse_ids`, or `None` if all weights
+        are be assumed to be 1.0.
+    combiner: A string specifying how to combine embedding results for each
+        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
+        the default.
+    default_id: The id to use for an entry with no features.
+    name: A name for this operation (optional).
+    partition_strategy: A string specifying the partitioning strategy.
+        Currently `"div"` and `"mod"` are supported. Default is `"div"`.
+
+
+  Returns:
+    Dense tensor of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
+
+  Raises:
+    ValueError: if `embedding_weights` is empty.
 
 
 - - -