formatting with yapf and pylint

Signed-off-by: Dom Miketa <[email protected]>

Author: d-miketa

onnx_tf/handlers/backend/depth_to_space.py

@@ -3,6 +3,7 @@
 import tensorflow as tf
 
 from onnx_tf.common import get_data_format
+from onnx_tf.common.tf_helper import tf_shape
 from onnx_tf.handlers.backend_handler import BackendHandler
 from onnx_tf.handlers.handler import onnx_op
 from onnx_tf.handlers.handler import tf_func
@@ -24,24 +25,27 @@ def version_1(cls, node, **kwargs):
     attrs = copy.deepcopy(node.attrs)
     attrs["data_format"] = storage_format
     return [
-        cls.make_tensor_from_onnx_node(
-            node, attrs=attrs, c_first_cuda_only=True, **kwargs)
+        cls.make_tensor_from_onnx_node(node,
+                                       attrs=attrs,
+                                       c_first_cuda_only=True,
+                                       **kwargs)
     ]
 
   @classmethod
   def _common(cls, node, **kwargs):
     x = kwargs["tensor_dict"][node.inputs[0]]
     x_rank = len(x.get_shape())
-    storage_format, compute_format = get_data_format(x_rank)
+    storage_format, _ = get_data_format(x_rank)
     attrs = copy.deepcopy(node.attrs)
     attrs["data_format"] = storage_format
     mode = attrs.get("mode", "DCR")
 
     if mode == "CRD":
       # need native computation
       bsize = attrs.get("blocksize")
-      x_shape = tf.shape(x)
-      batch, channel, height, width = x_shape[0], x_shape[1], x_shape[2], x_shape[3]
+      x_shape = tf_shape(x)
+      batch, channel = x_shape[0], x_shape[1]
+      height, width = x_shape[2], x_shape[3]
       csize = channel // (bsize**2)
 
       reshape_node = tf.reshape(x, [batch, csize, bsize, bsize, height, width])
@@ -51,11 +55,12 @@ def _common(cls, node, **kwargs):
                      [batch, csize, height * bsize, width * bsize])
       ]
 
-    else:
-      return [
-          cls.make_tensor_from_onnx_node(
-              node, attrs=attrs, c_first_cuda_only=True, **kwargs)
-      ]
+    return [
+        cls.make_tensor_from_onnx_node(node,
+                                       attrs=attrs,
+                                       c_first_cuda_only=True,
+                                       **kwargs)
+    ]
 
   @classmethod
   def version_11(cls, node, **kwargs):