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+### `tf.sparse_merge(sp_ids, sp_values, vocab_size, name=None)` {#sparse_merge}
+
+Combines a batch of feature ids and values into a single `SparseTensor`.
+
+The most common use case for this function occurs when feature ids and
+their corresponding values are stored in `Example` protos on disk.
+`parse_example` will return a batch of ids and a batch of values, and this
+function joins them into a single logical `SparseTensor` for use in
+functions such as `sparse_tensor_dense_matmul`, `sparse_to_dense`, etc.
+
+The `SparseTensor` returned by this function has the following properties:
+
+  - `indices` is equivalent to `sp_ids.indices` with the last
+    dimension discarded and replaced with `sp_ids.values`.
+  - `values` is simply `sp_values.values`.
+  - If `sp_ids.shape = [D0, D1, ..., Dn, K]`, then
+    `output.shape = [D0, D1, ..., Dn, vocab_size]`.
+
+For example, consider the following feature vectors:
+
+  vector1 = [-3, 0, 0, 0, 0, 0]
+  vector2 = [ 0, 1, 0, 4, 1, 0]
+  vector3 = [ 5, 0, 0, 9, 0, 0]
+
+These might be stored sparsely in the following Example protos by storing
+only the feature ids (column number if the vectors are treated as a matrix)
+of the non-zero elements and the corresponding values:
+
+  examples = [Example(features={
+                  "ids": Feature(int64_list=Int64List(value=[0])),
+                  "values": Feature(float_list=FloatList(value=[-3]))}),
+              Example(features={
+                  "ids": Feature(int64_list=Int64List(value=[1, 4, 3])),
+                  "values": Feature(float_list=FloatList(value=[1, 1, 4]))}),
+              Example(features={
+                  "ids": Feature(int64_list=Int64List(value=[0, 3])),
+                  "values": Feature(float_list=FloatList(value=[5, 9]))})]
+
+The result of calling parse_example on these examples will produce a
+dictionary with entries for "ids" and "values". Passing those two objects
+to this function along with vocab_size=6, will produce a `SparseTensor` that
+sparsely represents all three instances. Namely, the `indices` property will
+contain the coordinates of the non-zero entries in the feature matrix (the
+first dimension is the row number in the matrix, i.e., the index within the
+batch, and the second dimension is the column number, i.e., the feature id);
+`values` will contain the actual values. `shape` will be the shape of the
+original matrix, i.e., (3, 6). For our example above, the output will be
+equal to:
+
+  SparseTensor(indices=[[0, 0], [1, 1], [1, 3], [1, 4], [2, 0], [2, 3]],
+               values=[-3, 1, 4, 1, 5, 9],
+               shape=[3, 6])
+
+##### Args:
+
+
+*  <b>`sp_ids`</b>: A `SparseTensor` with `values` property of type `int32`
+    or `int64`.
+*  <b>`sp_values`</b>: A`SparseTensor` of any type.
+*  <b>`vocab_size`</b>: A scalar `int64` Tensor (or Python int) containing the new size
+    of the last dimension, `all(0 <= sp_ids.values < vocab_size)`.
+*  <b>`name`</b>: A name prefix for the returned tensors (optional)
+
+##### Returns:
+
+  A `SparseTensor` compactly representing a batch of feature ids and values,
+  useful for passing to functions that expect such a `SparseTensor`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_ids` or `sp_values` are not a `SparseTensor`.
+