Convert CRLF to LF in repo. Enforce correct line endings

.editorconfig

@@ -0,0 +1,16 @@
+root = true
+
+[*]
+indent_style = space
+indent_size = 4
+tab_width = 8
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+
+[*.{yml,yaml}]
+indent_size = 2
+
+[Makefile]
+indent_style = tab

.gitattributes

@@ -0,0 +1 @@
+* text=auto eol=lf

tests/handler/test_inference.py

@@ -1,37 +1,37 @@
-import torch
-
-from torch import multiprocessing as mp
-
-from tiktorch.handler.inference import IInference, InferenceProcess, run
-from tiktorch.tiktypes import TikTensor
-from tiktorch.rpc.mp import MPClient, Shutdown
-
-from tests.data.tiny_models import TinyConvNet2d
-
-
-def test_inference(tiny_model_2d):
-    config = tiny_model_2d["config"]
-    in_channels = config["input_channels"]
-    model = TinyConvNet2d(in_channels=in_channels)
-    inference = InferenceProcess(config=config, model=model)
-    data = TikTensor(torch.zeros(in_channels, 15, 15), (0,))
-    pred = inference.forward(data)
-    assert isinstance(pred.result(), TikTensor)
-    try:
-        inference.shutdown()
-    except Shutdown:
-        pass
-
-
-def test_inference_in_proc(tiny_model_2d, log_queue):
-    config = tiny_model_2d["config"]
-    in_channels = config["input_channels"]
-    model = TinyConvNet2d(in_channels=in_channels)
-    handler_conn, inference_conn = mp.Pipe()
-    p = mp.Process(target=run, kwargs={"conn": inference_conn, "model": model, "config": config, "log_queue": log_queue})
-    p.start()
-    client = MPClient(IInference(), handler_conn)
-    data = TikTensor(torch.zeros(in_channels, 15, 15), (0,))
-    f = client.forward(data)
+import torch
+
+from torch import multiprocessing as mp
+
+from tiktorch.handler.inference import IInference, InferenceProcess, run
+from tiktorch.tiktypes import TikTensor
+from tiktorch.rpc.mp import MPClient, Shutdown
+
+from tests.data.tiny_models import TinyConvNet2d
+
+
+def test_inference(tiny_model_2d):
+    config = tiny_model_2d["config"]
+    in_channels = config["input_channels"]
+    model = TinyConvNet2d(in_channels=in_channels)
+    inference = InferenceProcess(config=config, model=model)
+    data = TikTensor(torch.zeros(in_channels, 15, 15), (0,))
+    pred = inference.forward(data)
+    assert isinstance(pred.result(), TikTensor)
+    try:
+        inference.shutdown()
+    except Shutdown:
+        pass
+
+
+def test_inference_in_proc(tiny_model_2d, log_queue):
+    config = tiny_model_2d["config"]
+    in_channels = config["input_channels"]
+    model = TinyConvNet2d(in_channels=in_channels)
+    handler_conn, inference_conn = mp.Pipe()
+    p = mp.Process(target=run, kwargs={"conn": inference_conn, "model": model, "config": config, "log_queue": log_queue})
+    p.start()
+    client = MPClient(IInference(), handler_conn)
+    data = TikTensor(torch.zeros(in_channels, 15, 15), (0,))
+    f = client.forward(data)
     f.result()
     client.shutdown()