We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Tracking: mxnet 1.6
### To update

- [x] documentation
- [x] README.md
- [x] test-dependencies

### Fix

- [x] https://github.com/awslabs/gluon-ts/issues/583

### Other

- [x] Update `numpy~1.18`

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/gluonts/model/seq2seq/_forking_network.py`
Content:
```
1 # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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 # A copy of the License is located at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # or in the "license" file accompanying this file. This file is distributed
10 # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
11 # express or implied. See the License for the specific language governing
12 # permissions and limitations under the License.
13 
14 # Third-party imports
15 from mxnet import gluon, nd
16 
17 # First-party imports
18 from gluonts.block.decoder import Seq2SeqDecoder
19 from gluonts.block.enc2dec import Seq2SeqEnc2Dec
20 from gluonts.block.encoder import Seq2SeqEncoder
21 from gluonts.block.quantile_output import QuantileOutput
22 from gluonts.core.component import validated
23 from gluonts.model.common import Tensor
24 
25 nd_None = nd.array([])
26 
27 
28 class ForkingSeq2SeqNetworkBase(gluon.HybridBlock):
29     """
30     Base network for the :class:`ForkingSeq2SeqEstimator`.
31 
32     Parameters
33     ----------
34     encoder: Seq2SeqEncoder
35         encoder block
36     enc2dec: Seq2SeqEnc2Dec
37         encoder to decoder mapping block
38     decoder: Seq2SeqDecoder
39         decoder block
40     quantile_output: QuantileOutput
41         quantile output block
42     kwargs: dict
43         dictionary of Gluon HybridBlock parameters
44     """
45 
46     @validated()
47     def __init__(
48         self,
49         encoder: Seq2SeqEncoder,
50         enc2dec: Seq2SeqEnc2Dec,
51         decoder: Seq2SeqDecoder,
52         quantile_output: QuantileOutput,
53         **kwargs,
54     ) -> None:
55         super().__init__(**kwargs)
56 
57         self.encoder = encoder
58         self.enc2dec = enc2dec
59         self.decoder = decoder
60         self.quantile_output = quantile_output
61 
62         with self.name_scope():
63             self.quantile_proj = quantile_output.get_quantile_proj()
64             self.loss = quantile_output.get_loss()
65 
66 
67 class ForkingSeq2SeqTrainingNetwork(ForkingSeq2SeqNetworkBase):
68     # noinspection PyMethodOverriding
69     def hybrid_forward(
70         self, F, past_target: Tensor, future_target: Tensor
71     ) -> Tensor:
72         """
73         Parameters
74         ----------
75         F: mx.symbol or mx.ndarray
76             Gluon function space
77         past_target: Tensor
78             FIXME
79         future_target: Tensor
80             shape (num_ts, encoder_length, 1) FIXME
81 
82         Returns
83         -------
84         loss with shape (FIXME, FIXME)
85         """
86 
87         # FIXME: can we factor out a common prefix in the base network?
88         feat_static_real = nd_None
89         past_feat_dynamic_real = nd_None
90         future_feat_dynamic_real = nd_None
91 
92         enc_output_static, enc_output_dynamic = self.encoder(
93             past_target, feat_static_real, past_feat_dynamic_real
94         )
95 
96         dec_input_static, dec_input_dynamic, _ = self.enc2dec(
97             enc_output_static, enc_output_dynamic, future_feat_dynamic_real
98         )
99 
100         dec_output = self.decoder(dec_input_dynamic, dec_input_static)
101         dec_dist_output = self.quantile_proj(dec_output)
102 
103         loss = self.loss(future_target, dec_dist_output)
104         return loss.mean(axis=1)
105 
106 
107 class ForkingSeq2SeqPredictionNetwork(ForkingSeq2SeqNetworkBase):
108     # noinspection PyMethodOverriding
109     def hybrid_forward(self, F, past_target: Tensor) -> Tensor:
110         """
111         Parameters
112         ----------
113         F: mx.symbol or mx.ndarray
114             Gluon function space
115         past_target: Tensor
116             FIXME
117 
118         Returns
119         -------
120         prediction tensor with shape (FIXME, FIXME)
121         """
122 
123         # FIXME: can we factor out a common prefix in the base network?
124         feat_static_real = nd_None
125         past_feat_dynamic_real = nd_None
126         future_feat_dynamic_real = nd_None
127 
128         enc_output_static, enc_output_dynamic = self.encoder(
129             past_target, feat_static_real, past_feat_dynamic_real
130         )
131 
132         enc_output_static = (
133             nd_None if enc_output_static is None else enc_output_static
134         )
135 
136         dec_inp_static, dec_inp_dynamic, _ = self.enc2dec(
137             enc_output_static, enc_output_dynamic, future_feat_dynamic_real
138         )
139 
140         dec_output = self.decoder(dec_inp_dynamic, dec_inp_static)
141         fcst_output = F.slice_axis(dec_output, axis=1, begin=-1, end=None)
142         fcst_output = F.squeeze(fcst_output, axis=1)
143 
144         predictions = self.quantile_proj(fcst_output).swapaxes(2, 1)
145         return predictions
146
```
--- 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/src/gluonts/model/seq2seq/_forking_network.py b/src/gluonts/model/seq2seq/_forking_network.py
--- a/src/gluonts/model/seq2seq/_forking_network.py
+++ b/src/gluonts/model/seq2seq/_forking_network.py
@@ -12,7 +12,8 @@
 # permissions and limitations under the License.
 
 # Third-party imports
-from mxnet import gluon, nd
+import mxnet as mx
+from mxnet import gluon
 
 # First-party imports
 from gluonts.block.decoder import Seq2SeqDecoder
@@ -22,8 +23,6 @@
 from gluonts.core.component import validated
 from gluonts.model.common import Tensor
 
-nd_None = nd.array([])
-
 
 class ForkingSeq2SeqNetworkBase(gluon.HybridBlock):
     """
@@ -85,9 +84,9 @@
         """
 
         # FIXME: can we factor out a common prefix in the base network?
-        feat_static_real = nd_None
-        past_feat_dynamic_real = nd_None
-        future_feat_dynamic_real = nd_None
+        feat_static_real = F.zeros(shape=(1,))
+        past_feat_dynamic_real = F.zeros(shape=(1,))
+        future_feat_dynamic_real = F.zeros(shape=(1,))
 
         enc_output_static, enc_output_dynamic = self.encoder(
             past_target, feat_static_real, past_feat_dynamic_real
@@ -121,16 +120,18 @@
         """
 
         # FIXME: can we factor out a common prefix in the base network?
-        feat_static_real = nd_None
-        past_feat_dynamic_real = nd_None
-        future_feat_dynamic_real = nd_None
+        feat_static_real = F.zeros(shape=(1,))
+        past_feat_dynamic_real = F.zeros(shape=(1,))
+        future_feat_dynamic_real = F.zeros(shape=(1,))
 
         enc_output_static, enc_output_dynamic = self.encoder(
             past_target, feat_static_real, past_feat_dynamic_real
         )
 
         enc_output_static = (
-            nd_None if enc_output_static is None else enc_output_static
+            F.zeros(shape=(1,))
+            if enc_output_static is None
+            else enc_output_static
         )
 
         dec_inp_static, dec_inp_dynamic, _ = self.enc2dec(

{"golden_diff": "diff --git a/src/gluonts/model/seq2seq/_forking_network.py b/src/gluonts/model/seq2seq/_forking_network.py\n--- a/src/gluonts/model/seq2seq/_forking_network.py\n+++ b/src/gluonts/model/seq2seq/_forking_network.py\n@@ -12,7 +12,8 @@\n # permissions and limitations under the License.\n \n # Third-party imports\n-from mxnet import gluon, nd\n+import mxnet as mx\n+from mxnet import gluon\n \n # First-party imports\n from gluonts.block.decoder import Seq2SeqDecoder\n@@ -22,8 +23,6 @@\n from gluonts.core.component import validated\n from gluonts.model.common import Tensor\n \n-nd_None = nd.array([])\n-\n \n class ForkingSeq2SeqNetworkBase(gluon.HybridBlock):\n     \"\"\"\n@@ -85,9 +84,9 @@\n         \"\"\"\n \n         # FIXME: can we factor out a common prefix in the base network?\n-        feat_static_real = nd_None\n-        past_feat_dynamic_real = nd_None\n-        future_feat_dynamic_real = nd_None\n+        feat_static_real = F.zeros(shape=(1,))\n+        past_feat_dynamic_real = F.zeros(shape=(1,))\n+        future_feat_dynamic_real = F.zeros(shape=(1,))\n \n         enc_output_static, enc_output_dynamic = self.encoder(\n             past_target, feat_static_real, past_feat_dynamic_real\n@@ -121,16 +120,18 @@\n         \"\"\"\n \n         # FIXME: can we factor out a common prefix in the base network?\n-        feat_static_real = nd_None\n-        past_feat_dynamic_real = nd_None\n-        future_feat_dynamic_real = nd_None\n+        feat_static_real = F.zeros(shape=(1,))\n+        past_feat_dynamic_real = F.zeros(shape=(1,))\n+        future_feat_dynamic_real = F.zeros(shape=(1,))\n \n         enc_output_static, enc_output_dynamic = self.encoder(\n             past_target, feat_static_real, past_feat_dynamic_real\n         )\n \n         enc_output_static = (\n-            nd_None if enc_output_static is None else enc_output_static\n+            F.zeros(shape=(1,))\n+            if enc_output_static is None\n+            else enc_output_static\n         )\n \n         dec_inp_static, dec_inp_dynamic, _ = self.enc2dec(\n", "issue": "Tracking: mxnet 1.6\n### To update\r\n\r\n- [x] documentation\r\n- [x] README.md\r\n- [x] test-dependencies\r\n\r\n### Fix\r\n\r\n- [x] https://github.com/awslabs/gluon-ts/issues/583\r\n\r\n### Other\r\n\r\n- [x] Update `numpy~1.18`\n", "before_files": [{"content": "# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\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# A copy of the License is located at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# or in the \"license\" file accompanying this file. This file is distributed\n# on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\n# express or implied. See the License for the specific language governing\n# permissions and limitations under the License.\n\n# Third-party imports\nfrom mxnet import gluon, nd\n\n# First-party imports\nfrom gluonts.block.decoder import Seq2SeqDecoder\nfrom gluonts.block.enc2dec import Seq2SeqEnc2Dec\nfrom gluonts.block.encoder import Seq2SeqEncoder\nfrom gluonts.block.quantile_output import QuantileOutput\nfrom gluonts.core.component import validated\nfrom gluonts.model.common import Tensor\n\nnd_None = nd.array([])\n\n\nclass ForkingSeq2SeqNetworkBase(gluon.HybridBlock):\n    \"\"\"\n    Base network for the :class:`ForkingSeq2SeqEstimator`.\n\n    Parameters\n    ----------\n    encoder: Seq2SeqEncoder\n        encoder block\n    enc2dec: Seq2SeqEnc2Dec\n        encoder to decoder mapping block\n    decoder: Seq2SeqDecoder\n        decoder block\n    quantile_output: QuantileOutput\n        quantile output block\n    kwargs: dict\n        dictionary of Gluon HybridBlock parameters\n    \"\"\"\n\n    @validated()\n    def __init__(\n        self,\n        encoder: Seq2SeqEncoder,\n        enc2dec: Seq2SeqEnc2Dec,\n        decoder: Seq2SeqDecoder,\n        quantile_output: QuantileOutput,\n        **kwargs,\n    ) -> None:\n        super().__init__(**kwargs)\n\n        self.encoder = encoder\n        self.enc2dec = enc2dec\n        self.decoder = decoder\n        self.quantile_output = quantile_output\n\n        with self.name_scope():\n            self.quantile_proj = quantile_output.get_quantile_proj()\n            self.loss = quantile_output.get_loss()\n\n\nclass ForkingSeq2SeqTrainingNetwork(ForkingSeq2SeqNetworkBase):\n    # noinspection PyMethodOverriding\n    def hybrid_forward(\n        self, F, past_target: Tensor, future_target: Tensor\n    ) -> Tensor:\n        \"\"\"\n        Parameters\n        ----------\n        F: mx.symbol or mx.ndarray\n            Gluon function space\n        past_target: Tensor\n            FIXME\n        future_target: Tensor\n            shape (num_ts, encoder_length, 1) FIXME\n\n        Returns\n        -------\n        loss with shape (FIXME, FIXME)\n        \"\"\"\n\n        # FIXME: can we factor out a common prefix in the base network?\n        feat_static_real = nd_None\n        past_feat_dynamic_real = nd_None\n        future_feat_dynamic_real = nd_None\n\n        enc_output_static, enc_output_dynamic = self.encoder(\n            past_target, feat_static_real, past_feat_dynamic_real\n        )\n\n        dec_input_static, dec_input_dynamic, _ = self.enc2dec(\n            enc_output_static, enc_output_dynamic, future_feat_dynamic_real\n        )\n\n        dec_output = self.decoder(dec_input_dynamic, dec_input_static)\n        dec_dist_output = self.quantile_proj(dec_output)\n\n        loss = self.loss(future_target, dec_dist_output)\n        return loss.mean(axis=1)\n\n\nclass ForkingSeq2SeqPredictionNetwork(ForkingSeq2SeqNetworkBase):\n    # noinspection PyMethodOverriding\n    def hybrid_forward(self, F, past_target: Tensor) -> Tensor:\n        \"\"\"\n        Parameters\n        ----------\n        F: mx.symbol or mx.ndarray\n            Gluon function space\n        past_target: Tensor\n            FIXME\n\n        Returns\n        -------\n        prediction tensor with shape (FIXME, FIXME)\n        \"\"\"\n\n        # FIXME: can we factor out a common prefix in the base network?\n        feat_static_real = nd_None\n        past_feat_dynamic_real = nd_None\n        future_feat_dynamic_real = nd_None\n\n        enc_output_static, enc_output_dynamic = self.encoder(\n            past_target, feat_static_real, past_feat_dynamic_real\n        )\n\n        enc_output_static = (\n            nd_None if enc_output_static is None else enc_output_static\n        )\n\n        dec_inp_static, dec_inp_dynamic, _ = self.enc2dec(\n            enc_output_static, enc_output_dynamic, future_feat_dynamic_real\n        )\n\n        dec_output = self.decoder(dec_inp_dynamic, dec_inp_static)\n        fcst_output = F.slice_axis(dec_output, axis=1, begin=-1, end=None)\n        fcst_output = F.squeeze(fcst_output, axis=1)\n\n        predictions = self.quantile_proj(fcst_output).swapaxes(2, 1)\n        return predictions\n", "path": "src/gluonts/model/seq2seq/_forking_network.py"}], "after_files": [{"content": "# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\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# A copy of the License is located at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# or in the \"license\" file accompanying this file. This file is distributed\n# on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\n# express or implied. See the License for the specific language governing\n# permissions and limitations under the License.\n\n# Third-party imports\nimport mxnet as mx\nfrom mxnet import gluon\n\n# First-party imports\nfrom gluonts.block.decoder import Seq2SeqDecoder\nfrom gluonts.block.enc2dec import Seq2SeqEnc2Dec\nfrom gluonts.block.encoder import Seq2SeqEncoder\nfrom gluonts.block.quantile_output import QuantileOutput\nfrom gluonts.core.component import validated\nfrom gluonts.model.common import Tensor\n\n\nclass ForkingSeq2SeqNetworkBase(gluon.HybridBlock):\n    \"\"\"\n    Base network for the :class:`ForkingSeq2SeqEstimator`.\n\n    Parameters\n    ----------\n    encoder: Seq2SeqEncoder\n        encoder block\n    enc2dec: Seq2SeqEnc2Dec\n        encoder to decoder mapping block\n    decoder: Seq2SeqDecoder\n        decoder block\n    quantile_output: QuantileOutput\n        quantile output block\n    kwargs: dict\n        dictionary of Gluon HybridBlock parameters\n    \"\"\"\n\n    @validated()\n    def __init__(\n        self,\n        encoder: Seq2SeqEncoder,\n        enc2dec: Seq2SeqEnc2Dec,\n        decoder: Seq2SeqDecoder,\n        quantile_output: QuantileOutput,\n        **kwargs,\n    ) -> None:\n        super().__init__(**kwargs)\n\n        self.encoder = encoder\n        self.enc2dec = enc2dec\n        self.decoder = decoder\n        self.quantile_output = quantile_output\n\n        with self.name_scope():\n            self.quantile_proj = quantile_output.get_quantile_proj()\n            self.loss = quantile_output.get_loss()\n\n\nclass ForkingSeq2SeqTrainingNetwork(ForkingSeq2SeqNetworkBase):\n    # noinspection PyMethodOverriding\n    def hybrid_forward(\n        self, F, past_target: Tensor, future_target: Tensor\n    ) -> Tensor:\n        \"\"\"\n        Parameters\n        ----------\n        F: mx.symbol or mx.ndarray\n            Gluon function space\n        past_target: Tensor\n            FIXME\n        future_target: Tensor\n            shape (num_ts, encoder_length, 1) FIXME\n\n        Returns\n        -------\n        loss with shape (FIXME, FIXME)\n        \"\"\"\n\n        # FIXME: can we factor out a common prefix in the base network?\n        feat_static_real = F.zeros(shape=(1,))\n        past_feat_dynamic_real = F.zeros(shape=(1,))\n        future_feat_dynamic_real = F.zeros(shape=(1,))\n\n        enc_output_static, enc_output_dynamic = self.encoder(\n            past_target, feat_static_real, past_feat_dynamic_real\n        )\n\n        dec_input_static, dec_input_dynamic, _ = self.enc2dec(\n            enc_output_static, enc_output_dynamic, future_feat_dynamic_real\n        )\n\n        dec_output = self.decoder(dec_input_dynamic, dec_input_static)\n        dec_dist_output = self.quantile_proj(dec_output)\n\n        loss = self.loss(future_target, dec_dist_output)\n        return loss.mean(axis=1)\n\n\nclass ForkingSeq2SeqPredictionNetwork(ForkingSeq2SeqNetworkBase):\n    # noinspection PyMethodOverriding\n    def hybrid_forward(self, F, past_target: Tensor) -> Tensor:\n        \"\"\"\n        Parameters\n        ----------\n        F: mx.symbol or mx.ndarray\n            Gluon function space\n        past_target: Tensor\n            FIXME\n\n        Returns\n        -------\n        prediction tensor with shape (FIXME, FIXME)\n        \"\"\"\n\n        # FIXME: can we factor out a common prefix in the base network?\n        feat_static_real = F.zeros(shape=(1,))\n        past_feat_dynamic_real = F.zeros(shape=(1,))\n        future_feat_dynamic_real = F.zeros(shape=(1,))\n\n        enc_output_static, enc_output_dynamic = self.encoder(\n            past_target, feat_static_real, past_feat_dynamic_real\n        )\n\n        enc_output_static = (\n            F.zeros(shape=(1,))\n            if enc_output_static is None\n            else enc_output_static\n        )\n\n        dec_inp_static, dec_inp_dynamic, _ = self.enc2dec(\n            enc_output_static, enc_output_dynamic, future_feat_dynamic_real\n        )\n\n        dec_output = self.decoder(dec_inp_dynamic, dec_inp_static)\n        fcst_output = F.slice_axis(dec_output, axis=1, begin=-1, end=None)\n        fcst_output = F.squeeze(fcst_output, axis=1)\n\n        predictions = self.quantile_proj(fcst_output).swapaxes(2, 1)\n        return predictions\n", "path": "src/gluonts/model/seq2seq/_forking_network.py"}]}