Made theano/sandbox/linalg/ops.py flake8 compliant

theano/sandbox/linalg/ops.py

 from __future__ import absolute_import, print_function, division
 import logging
-
-logger = logging.getLogger(__name__)
-import numpy
-
 from six import iteritems, integer_types
 from six.moves import xrange
 
 from theano.gof import Op, Apply
 
-from theano.tensor import as_tensor_variable, dot, DimShuffle, Dot
+from theano.tensor import DimShuffle, Dot
 from theano.tensor.blas import Dot22
 from theano import tensor
 import theano.tensor
 from theano.tensor.opt import (register_stabilize,
-        register_specialize, register_canonicalize)
+                               register_specialize,
+                               register_canonicalize)
 from theano.gof import local_optimizer
 from theano.gof.opt import Optimizer
-from theano.gradient import DisconnectedType
-
-from theano.tensor.nlinalg import ( MatrixInverse,
-                                    matrix_inverse,
-                                    MatrixPinv,
-                                    pinv,
-                                    AllocDiag,
-                                    alloc_diag,
-                                    ExtractDiag,
-                                    extract_diag,
-                                    diag,
-                                    trace,
-                                    Det,
-                                    det,
-                                    Eig,
-                                    eig,
-                                    Eigh,
-                                    EighGrad,
-                                    eigh,
-                                    matrix_dot,
-                                    _zero_disconnected,
-                                    qr,
-                                    svd,
-                                    lstsq,
-                                    matrix_power,
-                                    norm
-                                    )
-
-from theano.tensor.slinalg import ( Cholesky,
-                                    cholesky,
-                                    CholeskyGrad,
-                                    Solve,
-                                    solve,
-                                    Eigvalsh,
-                                    EigvalshGrad,
-                                    eigvalsh
-                                    )
-
-try:
-    import scipy.linalg
-    imported_scipy = True
-except ImportError:
-    # some ops (e.g. Cholesky, Solve, A_Xinv_b) won't work
-    imported_scipy = False
+
+from theano.tensor.nlinalg import (MatrixInverse,
+                                   matrix_inverse,
+                                   extract_diag,
+                                   trace,
+                                   det)
+
+from theano.tensor.slinalg import (Cholesky,
+                                   cholesky,
+                                   Solve,
+                                   solve)
+
+logger = logging.getLogger(__name__)
 
 
 class Hint(Op):
@@ -212,8 +178,6 @@ class HintsFeature(object):
 class HintsOptimizer(Optimizer):
     """
     Optimizer that serves to add HintsFeature as an fgraph feature.
-    
-
     """
 
     def __init__(self):
@@ -280,8 +244,6 @@ def transinv_to_invtrans(node):
 @register_stabilize
 @local_optimizer([Dot, Dot22])
 def inv_as_solve(node):
-    if not imported_scipy:
-        return False
     if isinstance(node.op, (Dot, Dot22)):
         l, r = node.inputs
         if l.owner and l.owner.op == matrix_inverse:
@@ -310,8 +272,8 @@ def tag_solve_triangular(node):
                     return [Solve('lower_triangular')(A, b)]
                 else:
                     return [Solve('upper_triangular')(A, b)]
-            if (A.owner and isinstance(A.owner.op, DimShuffle)
-                and A.owner.op.new_order == (1, 0)):
+            if (A.owner and isinstance(A.owner.op, DimShuffle) and
+                    A.owner.op.new_order == (1, 0)):
                 A_T, = A.owner.inputs
                 if A_T.owner and isinstance(A_T.owner.op, type(cholesky)):
                     if A_T.owner.op.lower:
@@ -423,6 +385,5 @@ def spectral_radius_bound(X, log2_exponent):
     XX = X
     for i in xrange(log2_exponent):
         XX = tensor.dot(XX, XX)
-    return tensor.pow(
-            trace(XX),
-            2 ** (-log2_exponent))
+    return tensor.pow(trace(XX),
+                      2 ** (-log2_exponent))