Add power function and test to backend

tensornetwork/backends/abstract_backend.py

@@ -988,3 +988,26 @@ def deserialize_tensor(self, s: str) -> Tensor:
     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 
+        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 
+        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 
+        power of b.  Note that the shape of the returned tensor
+        is that produced by the broadcast of a and b.
+    """
+    raise NotImplementedError(
+        f"Backend {self.name} has not implemented power.")
+

tensornetwork/backends/backend_test.py

@@ -436,3 +436,8 @@ def test_pivot_not_implemented():
   backend = AbstractBackend()
   with pytest.raises(NotImplementedError):
     backend.pivot(np.ones((2, 2)))
+
+def test_power_not_implemented():
+  backend = AbstractBackend()
+  with pytest.raises(NotImplementedError):
+    backend.power(np.array([1, 2]), np.array([1, 2]))