We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Make RNG name configurable in Dropout
Currently `'dropout'` is hard-coded as an argument of `make_rng` inside `Dropout`. However, when implementing support for "recurrent dropout"  in an LSTMCell or similar you need two kinds of dropout:
1. A regular dropout which is applied to the inputs with a different mask at each step.
2. A "recurrent dropout" that is applied to the state with the same mask at each step.

To implement `2` a possibility is to set the RNG name to `'recurrent_dropout'` on the Dropout layer applied to the state and guarantee that each step uses the same random state. From `nn.scan`'s perspective the would look like this:

```python
nn.scan(..., split_rngs={'dropout': True, 'recurrent_dropout': False})
```
The proposal is to add an `rng_name` (or similar) attribute to `Dropout` so we are able support these kind of use-cases. The alternative would be to create a separate `RecurrentDropout` layer with the same code but different hard-coded value.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `flax/linen/stochastic.py`
Content:
```
1 # Copyright 2022 The Flax Authors.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 """Stochastic modules."""
16 
17 from typing import Optional, Sequence
18 
19 from flax.linen.module import compact
20 from flax.linen.module import merge_param
21 from flax.linen.module import Module
22 from jax import lax
23 from jax import random
24 import jax.numpy as jnp
25 
26 
27 class Dropout(Module):
28   """Create a dropout layer.
29 
30     Note: When using :meth:`Module.apply() <flax.linen.Module.apply>`, make sure
31     to include an RNG seed named `'dropout'`. For example::
32 
33       model.apply({'params': params}, inputs=inputs, train=True, rngs={'dropout': dropout_rng})`
34 
35     Attributes:
36       rate: the dropout probability.  (_not_ the keep rate!)
37       broadcast_dims: dimensions that will share the same dropout mask
38       deterministic: if false the inputs are scaled by `1 / (1 - rate)` and
39         masked, whereas if true, no mask is applied and the inputs are returned
40         as is.
41   """
42   rate: float
43   broadcast_dims: Sequence[int] = ()
44   deterministic: Optional[bool] = None
45 
46   @compact
47   def __call__(self, inputs, deterministic: Optional[bool] = None):
48     """Applies a random dropout mask to the input.
49 
50     Args:
51       inputs: the inputs that should be randomly masked.
52       deterministic: if false the inputs are scaled by `1 / (1 - rate)` and
53         masked, whereas if true, no mask is applied and the inputs are returned
54         as is.
55 
56     Returns:
57       The masked inputs reweighted to preserve mean.
58     """
59     deterministic = merge_param(
60         'deterministic', self.deterministic, deterministic)
61     if self.rate == 0.:
62       return inputs
63     # Prevent gradient NaNs in 1.0 edge-case.
64     if self.rate == 1.0:
65       return jnp.zeros_like(inputs)
66     keep_prob = 1. - self.rate
67     if deterministic:
68       return inputs
69     else:
70       rng = self.make_rng('dropout')
71       broadcast_shape = list(inputs.shape)
72       for dim in self.broadcast_dims:
73         broadcast_shape[dim] = 1
74       mask = random.bernoulli(rng, p=keep_prob, shape=broadcast_shape)
75       mask = jnp.broadcast_to(mask, inputs.shape)
76       return lax.select(mask, inputs / keep_prob, jnp.zeros_like(inputs))
77
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/flax/linen/stochastic.py b/flax/linen/stochastic.py
--- a/flax/linen/stochastic.py
+++ b/flax/linen/stochastic.py
@@ -38,10 +38,12 @@
       deterministic: if false the inputs are scaled by `1 / (1 - rate)` and
         masked, whereas if true, no mask is applied and the inputs are returned
         as is.
+      rng_collection: the rng collection name to use when requesting an rng key.
   """
   rate: float
   broadcast_dims: Sequence[int] = ()
   deterministic: Optional[bool] = None
+  rng_collection: str = 'dropout'
 
   @compact
   def __call__(self, inputs, deterministic: Optional[bool] = None):
@@ -67,7 +69,7 @@
     if deterministic:
       return inputs
     else:
-      rng = self.make_rng('dropout')
+      rng = self.make_rng(self.rng_collection)
       broadcast_shape = list(inputs.shape)
       for dim in self.broadcast_dims:
         broadcast_shape[dim] = 1

{"golden_diff": "diff --git a/flax/linen/stochastic.py b/flax/linen/stochastic.py\n--- a/flax/linen/stochastic.py\n+++ b/flax/linen/stochastic.py\n@@ -38,10 +38,12 @@\n       deterministic: if false the inputs are scaled by `1 / (1 - rate)` and\n         masked, whereas if true, no mask is applied and the inputs are returned\n         as is.\n+      rng_collection: the rng collection name to use when requesting an rng key.\n   \"\"\"\n   rate: float\n   broadcast_dims: Sequence[int] = ()\n   deterministic: Optional[bool] = None\n+  rng_collection: str = 'dropout'\n \n   @compact\n   def __call__(self, inputs, deterministic: Optional[bool] = None):\n@@ -67,7 +69,7 @@\n     if deterministic:\n       return inputs\n     else:\n-      rng = self.make_rng('dropout')\n+      rng = self.make_rng(self.rng_collection)\n       broadcast_shape = list(inputs.shape)\n       for dim in self.broadcast_dims:\n         broadcast_shape[dim] = 1\n", "issue": "Make RNG name configurable in Dropout\nCurrently `'dropout'` is hard-coded as an argument of `make_rng` inside `Dropout`. However, when implementing support for \"recurrent dropout\"  in an LSTMCell or similar you need two kinds of dropout:\r\n1. A regular dropout which is applied to the inputs with a different mask at each step.\r\n2. A \"recurrent dropout\" that is applied to the state with the same mask at each step.\r\n\r\nTo implement `2` a possibility is to set the RNG name to `'recurrent_dropout'` on the Dropout layer applied to the state and guarantee that each step uses the same random state. From `nn.scan`'s perspective the would look like this:\r\n\r\n```python\r\nnn.scan(..., split_rngs={'dropout': True, 'recurrent_dropout': False})\r\n```\r\nThe proposal is to add an `rng_name` (or similar) attribute to `Dropout` so we are able support these kind of use-cases. The alternative would be to create a separate `RecurrentDropout` layer with the same code but different hard-coded value.\n", "before_files": [{"content": "# Copyright 2022 The Flax Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Stochastic modules.\"\"\"\n\nfrom typing import Optional, Sequence\n\nfrom flax.linen.module import compact\nfrom flax.linen.module import merge_param\nfrom flax.linen.module import Module\nfrom jax import lax\nfrom jax import random\nimport jax.numpy as jnp\n\n\nclass Dropout(Module):\n  \"\"\"Create a dropout layer.\n\n    Note: When using :meth:`Module.apply() <flax.linen.Module.apply>`, make sure\n    to include an RNG seed named `'dropout'`. For example::\n\n      model.apply({'params': params}, inputs=inputs, train=True, rngs={'dropout': dropout_rng})`\n\n    Attributes:\n      rate: the dropout probability.  (_not_ the keep rate!)\n      broadcast_dims: dimensions that will share the same dropout mask\n      deterministic: if false the inputs are scaled by `1 / (1 - rate)` and\n        masked, whereas if true, no mask is applied and the inputs are returned\n        as is.\n  \"\"\"\n  rate: float\n  broadcast_dims: Sequence[int] = ()\n  deterministic: Optional[bool] = None\n\n  @compact\n  def __call__(self, inputs, deterministic: Optional[bool] = None):\n    \"\"\"Applies a random dropout mask to the input.\n\n    Args:\n      inputs: the inputs that should be randomly masked.\n      deterministic: if false the inputs are scaled by `1 / (1 - rate)` and\n        masked, whereas if true, no mask is applied and the inputs are returned\n        as is.\n\n    Returns:\n      The masked inputs reweighted to preserve mean.\n    \"\"\"\n    deterministic = merge_param(\n        'deterministic', self.deterministic, deterministic)\n    if self.rate == 0.:\n      return inputs\n    # Prevent gradient NaNs in 1.0 edge-case.\n    if self.rate == 1.0:\n      return jnp.zeros_like(inputs)\n    keep_prob = 1. - self.rate\n    if deterministic:\n      return inputs\n    else:\n      rng = self.make_rng('dropout')\n      broadcast_shape = list(inputs.shape)\n      for dim in self.broadcast_dims:\n        broadcast_shape[dim] = 1\n      mask = random.bernoulli(rng, p=keep_prob, shape=broadcast_shape)\n      mask = jnp.broadcast_to(mask, inputs.shape)\n      return lax.select(mask, inputs / keep_prob, jnp.zeros_like(inputs))\n", "path": "flax/linen/stochastic.py"}], "after_files": [{"content": "# Copyright 2022 The Flax Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Stochastic modules.\"\"\"\n\nfrom typing import Optional, Sequence\n\nfrom flax.linen.module import compact\nfrom flax.linen.module import merge_param\nfrom flax.linen.module import Module\nfrom jax import lax\nfrom jax import random\nimport jax.numpy as jnp\n\n\nclass Dropout(Module):\n  \"\"\"Create a dropout layer.\n\n    Note: When using :meth:`Module.apply() <flax.linen.Module.apply>`, make sure\n    to include an RNG seed named `'dropout'`. For example::\n\n      model.apply({'params': params}, inputs=inputs, train=True, rngs={'dropout': dropout_rng})`\n\n    Attributes:\n      rate: the dropout probability.  (_not_ the keep rate!)\n      broadcast_dims: dimensions that will share the same dropout mask\n      deterministic: if false the inputs are scaled by `1 / (1 - rate)` and\n        masked, whereas if true, no mask is applied and the inputs are returned\n        as is.\n      rng_collection: the rng collection name to use when requesting an rng key.\n  \"\"\"\n  rate: float\n  broadcast_dims: Sequence[int] = ()\n  deterministic: Optional[bool] = None\n  rng_collection: str = 'dropout'\n\n  @compact\n  def __call__(self, inputs, deterministic: Optional[bool] = None):\n    \"\"\"Applies a random dropout mask to the input.\n\n    Args:\n      inputs: the inputs that should be randomly masked.\n      deterministic: if false the inputs are scaled by `1 / (1 - rate)` and\n        masked, whereas if true, no mask is applied and the inputs are returned\n        as is.\n\n    Returns:\n      The masked inputs reweighted to preserve mean.\n    \"\"\"\n    deterministic = merge_param(\n        'deterministic', self.deterministic, deterministic)\n    if self.rate == 0.:\n      return inputs\n    # Prevent gradient NaNs in 1.0 edge-case.\n    if self.rate == 1.0:\n      return jnp.zeros_like(inputs)\n    keep_prob = 1. - self.rate\n    if deterministic:\n      return inputs\n    else:\n      rng = self.make_rng(self.rng_collection)\n      broadcast_shape = list(inputs.shape)\n      for dim in self.broadcast_dims:\n        broadcast_shape[dim] = 1\n      mask = random.bernoulli(rng, p=keep_prob, shape=broadcast_shape)\n      mask = jnp.broadcast_to(mask, inputs.shape)\n      return lax.select(mask, inputs / keep_prob, jnp.zeros_like(inputs))\n", "path": "flax/linen/stochastic.py"}]}