Add test for optimize_out_slice_nd

tests/test_TFNetworkRecLayer.py

@@ -3328,7 +3328,7 @@ def test_rec_subnet_simple_rnn():
     print("rnn_cell also fine.")
 
 
-def check_reclayer_optimize_out(subnet_layer_dict, other_subnet_layers=None, shared_base_net=None, rtol=1e-4):
+def check_reclayer_optimize_out(subnet_layer_dict, other_subnet_layers=None, shared_base_net=None, from_=None, rtol=1e-4):
   """
   :param dict[str] subnet_layer_dict: opts for the output layer inside the rec-layer subnet
   :param dict[str,dict[str]] other_subnet_layers: other layers for the rec-layer subnet
@@ -3344,7 +3344,7 @@ def check_reclayer_optimize_out(subnet_layer_dict, other_subnet_layers=None, sha
   subnet_layer_dict.setdefault("from", ["data:source"])
   rec_layer_dict = {
     "class": "rec",
-    "from": ["data"],
+    "from": ["data"] if from_ is None else [from_],
     "unit": {"output": subnet_layer_dict},
     "n_out": n_out,
     "is_output_layer": True
@@ -3598,6 +3598,31 @@ def test_reclayer_optimize_out_access_split():
     other_subnet_layers={"split": {"class": "split", "from": ["data:source"], "size_splits": [5, 8]}})
 
 
+def test_reclayer_optimize_out_slice_nd():
+  def random_start_positions(source, **kwargs):
+    import tensorflow as tf
+    enc = source(0, as_data=True, enforce_batch_major=True, auto_convert=False)
+    enc_shape = tf.shape(enc.placeholder)
+    enc_time_dim = enc_shape[enc.time_dim_axis]
+    return tf.random.uniform(enc_shape[:-1], 0, enc_time_dim-2, dtype=tf.dtypes.int32)
+
+  check_reclayer_optimize_out(
+    {"class": "linear", "activation": None, "from": ["encoder_reduced"]},
+    from_="position",
+    other_subnet_layers={
+      "window": {"class": "slice_nd", "from": "base:encoder", "start": "data:source", "size": None, "min_size": 1, "is_output_layer": True},
+      "encoder_reduced": {"class": "reduce", "mode": "sum", "axis": "T", "from": ["base:encoder"], "is_output_layer": True}
+      },
+    shared_base_net={
+      "encoder": {"class": "copy", "from": "data", "is_output_layer": True},
+      "position": {
+        "class": "eval", "from": "encoder", "is_output_layer": True,
+        "eval": random_start_positions,
+        "out_type": {"batch_dim_axis": 0, "time_dim_axis": 1, "shape": (None,), "sparse": True, "dtype": "int32", "dim": None}}
+      }
+    )
+
+
 def test_reclayer_att_with_kv_in_rec():
   net_dict = {
     'decision': {'class': 'decide', 'from': ['output'], 'loss': 'edit_distance', 'loss_opts': {}, 'target': 'classes'},