formatting

Author: mganahl

tensornetwork/backends/abstract_backend.py

@@ -964,47 +964,47 @@ def pivot(self, tensor: Tensor, pivot_axis: int = -1) -> Tensor:
   def serialize_tensor(self, tensor: Tensor) -> str:
     """
     Return a string that serializes the given tensor.
-    
+
     Args:
       tensor: The input tensor.
-      
+
     Returns:
-      A string representing the serialized tensor. 
+      A string representing the serialized tensor.
     """
     raise NotImplementedError(
         "Backend '{}' has not implemented serialize_tensor.".format(self.name))
 
   def deserialize_tensor(self, s: str) -> Tensor:
     """
-    Return a tensor given a serialized tensor string. 
-    
+    Return a tensor given a serialized tensor string.
+
     Args:
       s: The input string representing a serialized tensor.
-      
+
     Returns:
       The tensor object represented by the string.
-     
+
     """
     raise NotImplementedError(
         "Backend '{}' has not implemented deserialize_tensor.".format(
             self.name))
 
   def power(self, a: Tensor, b: Union[Tensor, float]) -> Tensor:
     """
-    Returns the exponentiation of tensor a raised to b.  
-      If b is a tensor, then the exponentiation is element-wise 
+    Returns the exponentiation of tensor a raised to b.
+      If b is a tensor, then the exponentiation is element-wise
         between the two tensors, with a as the base and b as the power.
-        Note that a and b must be broadcastable to the same shape if 
+        Note that a and b must be broadcastable to the same shape if
         b is a tensor.
       If b is a scalar, then the exponentiation is each value in a
         raised to the power of b.
-    
+
     Args:
       a: The tensor containing the bases.
       b: The tensor containing the powers; or a single scalar as the power.
 
     Returns:
-      The tensor that is each element of a raised to the 
+      The tensor that is each element of a raised to the
         power of b.  Note that the shape of the returned tensor
         is that produced by the broadcast of a and b.
     """