Remove unnecessary dicts from keyword specifications

aesara/ifelse.py

@@ -149,7 +149,7 @@ class IfElse(_NoPythonOp):
             new_outs = new_ifelse(
                 node.inputs[0],
                 *(new_ts_inputs + new_fs_inputs),
-                **dict(return_list=True),
+                return_list=True,
             )
         else:
             new_outs = []
@@ -203,7 +203,7 @@ class IfElse(_NoPythonOp):
         return Apply(self, [c] + list(args), [t.type() for t in aes])
 
     def R_op(self, inputs, eval_points):
-        return self(inputs[0], *eval_points[1:], **dict(return_list=True))
+        return self(inputs[0], *eval_points[1:], return_list=True)
 
     def grad(self, ins, grads):
         aes = ins[1:][: self.n_outs]
@@ -244,8 +244,8 @@ class IfElse(_NoPythonOp):
         condition_grad = condition.zeros_like().astype(config.floatX)
         return (
             [condition_grad]
-            + if_true_op(*if_true, **dict(return_list=True))
-            + if_false_op(*if_false, **dict(return_list=True))
+            + if_true_op(*if_true, return_list=True)
+            + if_false_op(*if_false, return_list=True)
         )
 
     def make_thunk(self, node, storage_map, compute_map, no_recycling, impl=None):
@@ -407,7 +407,7 @@ def ifelse(condition, then_branch, else_branch, name=None):
     new_ifelse = IfElse(n_outs=len(then_branch), as_view=False, gpu=False, name=name)
 
     ins = [condition] + list(new_then_branch) + list(new_else_branch)
-    rval = new_ifelse(*ins, **dict(return_list=True))
+    rval = new_ifelse(*ins, return_list=True)
 
     if rval_type is None:
         return rval[0]
@@ -432,7 +432,7 @@ def cond_make_inplace(fgraph, node):
         )
     ):
         return IfElse(n_outs=op.n_outs, as_view=True, gpu=op.gpu, name=op.name)(
-            *node.inputs, **dict(return_list=True)
+            *node.inputs, return_list=True
         )
     return False
 
@@ -533,8 +533,8 @@ def ifelse_lift_single_if_through_acceptable_ops(fgraph, main_node):
         else:
             true_ins.append(x)
             false_ins.append(x)
-    true_eval = mop(*true_ins, **dict(return_list=True))
-    false_eval = mop(*false_ins, **dict(return_list=True))
+    true_eval = mop(*true_ins, return_list=True)
+    false_eval = mop(*false_ins, return_list=True)
     # true_eval  = clone_replace(outs, replace = dict(zip(node.outputs, aes)))
     # false_eval = clone_replace(outs, replace = dict(zip(node.outputs, fs)))
 
@@ -566,7 +566,7 @@ def cond_merge_ifs_true(fgraph, node):
     old_ins = list(node.inputs)
     for pos, var in replace.items():
         old_ins[pos] = var
-    return op(*old_ins, **dict(return_list=True))
+    return op(*old_ins, return_list=True)
 
 
 @local_optimizer([IfElse])
@@ -593,7 +593,7 @@ def cond_merge_ifs_false(fgraph, node):
     old_ins = list(node.inputs)
     for pos, var in replace.items():
         old_ins[pos] = var
-    return op(*old_ins, **dict(return_list=True))
+    return op(*old_ins, return_list=True)
 
 
 class CondMerge(GlobalOptimizer):
@@ -635,7 +635,7 @@ class CondMerge(GlobalOptimizer):
                     name=mn_name + "&" + pl_name,
                 )
                 print("here")
-                new_outs = new_ifelse(*new_ins, **dict(return_list=True))
+                new_outs = new_ifelse(*new_ins, return_list=True)
                 new_outs = [clone_replace(x) for x in new_outs]
                 old_outs = []
                 if type(merging_node.outputs) not in (list, tuple):
@@ -684,7 +684,7 @@ def cond_remove_identical(fgraph, node):
     new_ifelse = IfElse(n_outs=len(nw_ts), as_view=op.as_view, gpu=op.gpu, name=op.name)
 
     new_ins = [node.inputs[0]] + nw_ts + nw_fs
-    new_outs = new_ifelse(*new_ins, **dict(return_list=True))
+    new_outs = new_ifelse(*new_ins, return_list=True)
 
     rval = []
     for idx in range(len(node.outputs)):
@@ -736,7 +736,7 @@ def cond_merge_random_op(fgraph, main_node):
                 gpu=False,
                 name=mn_name + "&" + pl_name,
             )
-            new_outs = new_ifelse(*new_ins, **dict(return_list=True))
+            new_outs = new_ifelse(*new_ins, return_list=True)
             old_outs = []
             if type(merging_node.outputs) not in (list, tuple):
                 old_outs += [merging_node.outputs]

aesara/scan/opt.py

@@ -218,7 +218,7 @@ def remove_constants_and_unused_inputs_scan(fgraph, node):
         op_outs = clone_replace(op_outs, replace=givens)
         nw_info = dataclasses.replace(op.info, n_seqs=nw_n_seqs)
         nwScan = Scan(nw_inner, op_outs, nw_info, op.mode)
-        nw_outs = nwScan(*nw_outer, **dict(return_list=True))
+        nw_outs = nwScan(*nw_outer, return_list=True)
         return dict([("remove", [node])] + list(zip(node.outputs, nw_outs)))
     else:
         return False
@@ -399,9 +399,7 @@ class PushOutNonSeqScan(GlobalOptimizer):
             nwScan = Scan(op_ins, op_outs, op.info, op.mode)
 
             # Do not call make_node for test_value
-            nw_node = nwScan(*(node.inputs + nw_outer), **dict(return_list=True))[
-                0
-            ].owner
+            nw_node = nwScan(*(node.inputs + nw_outer), return_list=True)[0].owner
 
             fgraph.replace_all_validate_remove(
                 list(zip(node.outputs, nw_node.outputs)),
@@ -672,7 +670,7 @@ class PushOutSeqScan(GlobalOptimizer):
             # Do not call make_node for test_value
             nw_node = nwScan(
                 *(node.inputs[:1] + nw_outer + node.inputs[1:]),
-                **dict(return_list=True),
+                return_list=True,
             )[0].owner
 
             fgraph.replace_all_validate_remove(
@@ -958,9 +956,9 @@ class PushOutScanOutput(GlobalOptimizer):
         )
 
         # Create the Apply node for the scan op
-        new_scan_node = new_scan_op(
-            *new_scan_args.outer_inputs, **dict(return_list=True)
-        )[0].owner
+        new_scan_node = new_scan_op(*new_scan_args.outer_inputs, return_list=True)[
+            0
+        ].owner
 
         # Modify the outer graph to make sure the outputs of the new scan are
         # used instead of the outputs of the old scan
@@ -1071,7 +1069,7 @@ class ScanInplaceOptimizer(GlobalOptimizer):
         new_op.destroy_map = destroy_map
 
         # Do not call make_node for test_value
-        new_outs = new_op(*inputs, **dict(return_list=True))
+        new_outs = new_op(*inputs, return_list=True)
         try:
             fgraph.replace_all_validate_remove(
                 list(zip(node.outputs, new_outs)),
@@ -1595,9 +1593,7 @@ class ScanSaveMem(GlobalOptimizer):
                 return
 
             # Do not call make_node for test_value
-            new_outs = Scan(inps, outs, info, op.mode)(
-                *node_ins, **dict(return_list=True)
-            )
+            new_outs = Scan(inps, outs, info, op.mode)(*node_ins, return_list=True)
 
             old_new = []
             # 3.7 Get replace pairs for those outputs that do not change

aesara/tensor/basic.py

@@ -771,7 +771,7 @@ class Rebroadcast(COp):
     def R_op(self, inputs, eval_points):
         if eval_points[0] is None:
             return [None]
-        return self(*eval_points, **dict(return_list=True))
+        return self(*eval_points, return_list=True)
 
     def c_code(self, node, nodename, inp, out, sub):
         (iname,) = inp
@@ -1542,7 +1542,7 @@ class Alloc(COp):
     def R_op(self, inputs, eval_points):
         if eval_points[0] is None:
             return [None]
-        return self(eval_points[0], *inputs[1:], **dict(return_list=True))
+        return self(eval_points[0], *inputs[1:], return_list=True)
 
     def do_constant_folding(self, fgraph, node):
         clients = fgraph.clients[node.outputs[0]]
@@ -1945,7 +1945,7 @@ class Split(COp):
     def grad(self, inputs, g_outputs):
         """Join the gradients along the axis that was used to split x."""
         x, axis, n = inputs
-        outputs = self(*inputs, **dict(return_list=True))
+        outputs = self(*inputs, return_list=True)
         # If all the output gradients are disconnected, then so are the inputs
         if builtins.all([isinstance(g.type, DisconnectedType) for g in g_outputs]):
             return [

aesara/tensor/basic_opt.py

@@ -465,7 +465,7 @@ class InplaceElemwiseOptimizer(GlobalOptimizer):
                                 )
                             )
                         new_outputs = self.op(new_scal, inplace_pattern)(
-                            *node.inputs, **dict(return_list=True)
+                            *node.inputs, return_list=True
                         )
                         new_node = new_outputs[0].owner
 
@@ -684,7 +684,7 @@ def local_dimshuffle_lift(fgraph, node):
             new_inp = op.__class__(inp.type.broadcastable, op.new_order)(inp)
             new_inputs.append(apply_local_dimshuffle_lift(fgraph, new_inp))
         copy_stack_trace(node.outputs[0], new_inputs)
-        ret = inode.op(*new_inputs, **dict(return_list=True))
+        ret = inode.op(*new_inputs, return_list=True)
         return ret
     if inode and isinstance(inode.op, DimShuffle):
         new_order = [x == "x" and "x" or inode.op.new_order[x] for x in new_order]

aesara/tensor/elemwise.py

@@ -274,7 +274,7 @@ class DimShuffle(ExternalCOp):
     def R_op(self, inputs, eval_points):
         if None in eval_points:
             return [None]
-        return self(*eval_points, **dict(return_list=True))
+        return self(*eval_points, return_list=True)
 
     def grad(self, inp, grads):
         from aesara.tensor.basic import as_tensor_variable
@@ -504,7 +504,7 @@ second dimension
             return self.name
 
     def R_op(self, inputs, eval_points):
-        outs = self(*inputs, **dict(return_list=True))
+        outs = self(*inputs, return_list=True)
         rval = [None for x in outs]
         # For each output
         for idx, out in enumerate(outs):

aesara/tensor/math.py

@@ -2462,7 +2462,7 @@ class Sum(CAReduceDtype):
         # part of self
         if None in eval_points:
             return [None]
-        return self(*eval_points, **dict(return_list=True))
+        return self(*eval_points, return_list=True)
 
 
 def sum(input, axis=None, dtype=None, keepdims=False, acc_dtype=None):

aesara/tensor/shape.py

@@ -577,7 +577,7 @@ class Reshape(COp):
     def R_op(self, inputs, eval_points):
         if eval_points[0] is None:
             return [None]
-        return self(eval_points[0], *inputs[1:], **dict(return_list=True))
+        return self(eval_points[0], *inputs[1:], return_list=True)
 
     def infer_shape(self, fgraph, node, ishapes):
         from aesara.tensor.math import eq, maximum, mul

aesara/tensor/subtensor.py

@@ -1149,7 +1149,7 @@ class Subtensor(COp):
         # (they should be defaulted to zeros_like by the global R_op)
         if eval_points[0] is None:
             return [None]
-        return self(eval_points[0], *inputs[1:], **dict(return_list=True))
+        return self(eval_points[0], *inputs[1:], return_list=True)
 
 
 class SubtensorPrinter:
@@ -1764,9 +1764,7 @@ class IncSubtensor(COp):
             return [None]
         # Again we ignore eval points for indices because incsubtensor is
         # not differentiable wrt to those
-        return self(
-            eval_points[0], eval_points[1], *inputs[2:], **dict(return_list=True)
-        )
+        return self(eval_points[0], eval_points[1], *inputs[2:], return_list=True)
 
     def connection_pattern(self, node):
 

tests/gpuarray/test_elemwise.py

@@ -41,7 +41,7 @@ class TestGpuBroadcast(test_elemwise.TestBroadcast):
     linkers = [PerformLinker, CLinker]
 
     def rand_cval(self, shp):
-        return rand_gpuarray(*shp, **dict(cls=gpuarray))
+        return rand_gpuarray(*shp, cls=gpuarray)
 
 
 def test_elemwise_pow():