Update print warnings to use warnings.warn

tests/tensor/test_blas_c.py

-import sys
+from warnings import warn
 
 import numpy as np
 import pytest
@@ -197,7 +197,7 @@ class TestCGemv(OptimizationTestMixin):
 
     def test_force_gemv_init(self):
         if check_force_gemv_init():
-            sys.stderr.write(
+            warn(
                 "WARNING: The current BLAS requires Theano to initialize"
                 + " memory for some GEMV calls which will result in a minor"
                 + " degradation in performance for such calls."

theano/compile/debugmode.py

@@ -13,6 +13,7 @@ import sys
 from io import StringIO
 from itertools import chain
 from itertools import product as itertools_product
+from warnings import warn
 
 import numpy as np
 
@@ -934,7 +935,7 @@ def _check_strides_match(a, b, warn_err, op):
         if warn_err == 2:
             raise e
         else:
-            print("WARNING:", e, file=sys.stderr)
+            warn(str(e))
 
 
 def _lessbroken_deepcopy(a):
@@ -2459,7 +2460,7 @@ class _Maker(FunctionMaker):  # inheritance buys a few helper functions
                 l0 = fgraph0.equivalence_tracker.event_list
                 if li != l0:
                     infolog = StringIO()
-                    print("WARNING: Optimization process is unstable...", file=infolog)
+                    print("Optimization process is unstable...", file=infolog)
                     print(
                         "  (HINT: Ops that the nodes point to must compare " "equal)",
                         file=infolog,

theano/compile/mode.py

@@ -123,7 +123,7 @@ class AddDestroyHandler(gof.GlobalOptimizer):
                 break
         if not supervisor_added:
             warnings.warn(
-                "WARNING: Supervisor is not added. Please build a FunctionGraph"
+                "Supervisor is not added. Please build a FunctionGraph"
                 "via theano.compile.function.types.std_graph()"
                 "or add the Supervisor class manually.",
                 stacklevel=3,

theano/gof/cmodule.py

@@ -2014,7 +2014,7 @@ class GCC_compiler(Compiler):
             and "icpc" not in theano.config.cxx
         ):
             _logger.warning(
-                "OPTIMIZATION WARNING: your Theano flag `cxx` seems not to be"
+                "Your Theano flag `cxx` seems not to be"
                 " the g++ compiler. So we disable the compiler optimization"
                 " specific to g++ that tell to compile for a specific CPU."
                 " At worst, this could cause slow down.\n"
@@ -2083,7 +2083,7 @@ class GCC_compiler(Compiler):
                 else:
                     reported_lines = native_lines
                 _logger.warning(
-                    "OPTIMIZATION WARNING: Theano was not able to find the"
+                    "Theano was not able to find the"
                     " g++ parameters that tune the compilation to your "
                     " specific CPU. This can slow down the execution of Theano"
                     " functions. Please submit the following lines to"
@@ -2095,7 +2095,7 @@ class GCC_compiler(Compiler):
                 _logger.info(f"g++ default lines: {default_lines}")
                 if len(default_lines) < 1:
                     _logger.warning(
-                        "OPTIMIZATION WARNING: Theano was not able to find the"
+                        "Theano was not able to find the"
                         " default g++ parameters. This is needed to tune"
                         " the compilation to your specific"
                         " CPU. This can slow down the execution of Theano"

theano/gof/link.py

@@ -3,6 +3,7 @@ import traceback
 from copy import copy, deepcopy
 from io import StringIO
 from sys import getsizeof
+from warnings import warn
 
 import numpy as np
 
@@ -334,7 +335,7 @@ def raise_with_op(fgraph, node, thunk=None, exc_info=None, storage_map=None):
             str(exc_value) + detailed_err_msg + "\n" + "\n".join(hints)
         )
     except TypeError:
-        print(f"WARNING: {exc_type} error does not allow us to add extra error message")
+        warn(f"{exc_type} error does not allow us to add extra error message")
         # Some exception need extra parameter in inputs. So forget the
         # extra long error message in that case.
     raise exc_value.with_traceback(exc_trace)

theano/gof/toolbox.py

@@ -610,17 +610,15 @@ class ReplaceValidate(History, Validator):
             if rm in fgraph.apply_nodes or rm in fgraph.variables:
                 fgraph.revert(chk)
                 if warn:
-                    out = sys.stderr
-                    print(
-                        "WARNING: An optimization wanted to replace a Variable"
+                    warn(
+                        "An optimization wanted to replace a Variable"
                         " in the graph, but the replacement for it doesn't"
                         " remove it. We disabled the optimization."
                         " Your function runs correctly, but it would be"
                         " appreciated if you submit this problem to the"
-                        " mailing list theano-users so that we can fix it.",
-                        file=out,
+                        " mailing list theano-users so that we can fix it."
+                        f"{reason}: {replacements}",
                     )
-                    print(reason, replacements, file=out)
                 raise ReplacementDidNotRemoveError()
 
     def __getstate__(self):

theano/gof/utils.py

@@ -3,6 +3,7 @@ import linecache
 import sys
 import traceback
 from io import StringIO
+from warnings import warn
 
 from theano import config
 
@@ -397,7 +398,10 @@ def deprecated(filename, msg=""):
 
         def g(*args, **kwargs):
             if printme[0]:
-                print(f"WARNING: {filename}.{f.__name__} deprecated. {msg}")
+                warn(
+                    f"{filename}.{f.__name__} deprecated. {msg}",
+                    category=DeprecationWarning,
+                )
                 printme[0] = False
             return f(*args, **kwargs)
 

theano/gpuarray/__init__.py

@@ -24,11 +24,21 @@ try:
 except ImportError:
     pygpu = None
 
-from . import ctc, dnn, extra_ops, fft, multinomial, opt, reduction, rng_mrg, sort
-from .basic_ops import as_gpuarray_variable
+from theano.gpuarray import (
+    ctc,
+    dnn,
+    extra_ops,
+    fft,
+    multinomial,
+    opt,
+    reduction,
+    rng_mrg,
+    sort,
+)
+from theano.gpuarray.basic_ops import as_gpuarray_variable
 
 # This is for documentation not to depend on the availability of pygpu
-from .type import (
+from theano.gpuarray.type import (
     ContextNotDefined,
     GpuArrayConstant,
     GpuArraySharedVariable,
@@ -160,8 +170,8 @@ def init_dev(dev, name=None, preallocate=None):
                     f" {int(context.free_gmem / MB)} MB are available."
                 )
             elif gmem > context.free_gmem - 50 * MB:
-                print(
-                    "WARNING: Preallocating too much memory can prevent cudnn and cublas from working properly"
+                warnings.warn(
+                    "Preallocating too much memory can prevent cudnn and cublas from working properly"
                 )
 
             # This will allocate and immediately free an object of size gmem

theano/gradient.py

@@ -2157,6 +2157,7 @@ def consider_constant(x):
             "consider_constant() is deprecated, use zero_grad() or "
             "disconnected_grad() instead."
         ),
+        category=DeprecationWarning,
         stacklevel=3,
     )
 

theano/sandbox/blocksparse.py

@@ -22,7 +22,7 @@ __all__ = [
 ]
 
 warnings.warn(
-    "DEPRECATION: theano.sandbox.blocksparse does not exist anymore,"
+    "theano.sandbox.blocksparse does not exist anymore,"
     "it has been moved to theano.tensor.nnet.blocksparse.",
     category=DeprecationWarning,
 )

theano/sandbox/conv.py

-import sys
+from warnings import warn
 
 
-print(
-    "DEPRECATION: theano.sandbox.conv no longer provides conv. "
+warn(
+    "theano.sandbox.conv no longer provides conv. "
     "They have been moved to theano.tensor.nnet.conv",
-    file=sys.stderr,
+    category=DeprecationWarning,
 )

theano/sandbox/softsign.py

 from theano.tensor.nnet.nnet import softsign  # noqa
-import sys
+from warnings import warn
 
 
-print(
-    "DEPRECATION WARNING: softsign was moved from theano.sandbox.softsign to "
-    "theano.tensor.nnet.nnet ",
-    file=sys.stderr,
+warn(
+    "softsign was moved from theano.sandbox.softsign to theano.tensor.nnet.nnet ",
+    category=DeprecationWarning,
 )

theano/scan/basic.py

@@ -377,9 +377,8 @@ def scan(
     for i in range(n_outs):
         if outs_info[i] is not None:
             if isinstance(outs_info[i], dict):
-                # DEPRECATED :
                 if outs_info[i].get("return_steps", None) is not None:
-                    raise ValueError(
+                    raise DeprecationWarning(
                         "Using `return_steps` has been deprecated. "
                         "Simply select the entries you need using a "
                         "subtensor. Scan will optimize memory "
@@ -396,12 +395,9 @@ def scan(
             ):
                 # ^ no initial state but taps provided
                 raise ValueError(
-                    (
-                        "If you are using slices of an output "
-                        "you need to provide a initial state "
-                        "for it"
-                    ),
-                    outs_info[i],
+                    "If you are using slices of an output "
+                    "you need to provide a initial state "
+                    f"for it: {outs_info[i]}"
                 )
             elif (
                 outs_info[i].get("initial", None) is not None

theano/scan/opt.py

@@ -101,14 +101,6 @@ list_opt_slice = [
 ]
 
 
-def warning(*msg):
-    _logger.warning("WARNING theano.scan: " + " ".join(msg))
-
-
-def info(*msg):
-    _logger.info("INFO theano.scan: " + " ".join(msg))
-
-
 @gof.local_optimizer([Scan])
 def remove_constants_and_unused_inputs_scan(fgraph, node):
     """

theano/scan/utils.py

@@ -202,7 +202,8 @@ def clone(
     """
     if copy_inputs is not DEPRECATED_ARG:
         warnings.warn(
-            "In `clone()` function, the argument `copy_inputs` has been deprecated and renamed into `share_inputs`"
+            "In `clone()` function, the argument `copy_inputs` has been deprecated and renamed into `share_inputs`",
+            category=DeprecationWarning,
         )
         assert share_inputs  # since we used `copy_inputs` we should have default value for `share_inputs`
         share_inputs = copy_inputs

theano/sparse/__init__.py

-import sys
+from warnings import warn
 
 
 try:
@@ -7,16 +7,10 @@ try:
     scipy_ver = [int(n) for n in scipy.__version__.split(".")[:2]]
     enable_sparse = bool(scipy_ver >= [0, 7])
     if not enable_sparse:
-        sys.stderr.write(
-            f"WARNING: scipy version = {scipy.__version__}."
-            " We request version >=0.7.0 for the sparse code as it has"
-            " bugs fixed in the sparse matrix code.\n"
-        )
+        warn(f"SciPy version is {scipy.__version__}.  We recommend a version >= 0.7.0")
 except ImportError:
     enable_sparse = False
-    sys.stderr.write(
-        "WARNING: scipy can't be imported." " We disable the sparse matrix code."
-    )
+    warn("scipy can't be imported." " We disable the sparse matrix code.")
 
 from theano.sparse.type import *
 

theano/sparse/basic.py

@@ -3,13 +3,11 @@ Classes for handling sparse matrices.
 
 To read about different sparse formats, see
 http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps
-"""
-
 
-# TODO
-# Automatic methods for determining best sparse format?
+TODO: Automatic methods for determining best sparse format?
 
-import sys
+"""
+from warnings import warn
 
 import numpy as np
 import scipy.sparse
@@ -907,9 +905,8 @@ class DenseFromSparse(gof.op.Op):
         (x,) = inputs
         (out,) = outputs
         if _is_dense(x):
-            print(
-                ("WARNING: You just called DenseFromSparse on a dense matrix."),
-                file=sys.stderr,
+            warn(
+                "You just called DenseFromSparse on a dense matrix.",
             )
             out[0] = x
         else:
@@ -2398,10 +2395,8 @@ class MulSD(gof.op.Op):
                         z_data[j_idx] *= y[i, j]
                 out[0] = z
             else:
-                print(
-                    ("WARNING: crappy implementation of MulSD"),
-                    x.format,
-                    file=sys.stderr,
+                warn(
+                    "This implementation of MulSD is deficient: {x.format}",
                 )
                 out[0] = type(x)(x.toarray() * y)
 

theano/tensor/basic.py

@@ -5455,7 +5455,10 @@ def flatten(x, ndim=None, outdim=None):
     elif outdim is not None and ndim is not None:
         raise ValueError("You should only specify ndim")
     elif outdim is not None:
-        warnings.warn("flatten outdim parameter is deprecated, use ndim instead.")
+        warnings.warn(
+            "flatten outdim parameter is deprecated, use ndim instead.",
+            category=DeprecationWarning,
+        )
 
         ndim = outdim
     # Any input variable can be flattened to have ndim of 1,

theano/tensor/nnet/conv.py

@@ -554,12 +554,12 @@ class ConvOp(OpenMPOp):
                     new -= 1
 
                 warnstr = (
-                    "OPTIMISATION WARNING: in ConvOp.__init__() "
-                    "unroll_batch(%i) must be 0 or a divisor of"
-                    " bsize(%i). We revert it to %i. This"
+                    "In ConvOp.__init__(): "
+                    f"unroll_batch({self.unroll_batch}) must be 0 or a divisor of"
+                    f" bsize({self.bsize}). We revert it to {new}. This"
                     " won't change the result, but may make it slower."
                 )
-                _logger.warning(warnstr, self.unroll_batch, self.bsize, new)
+                _logger.warning(warnstr)
 
                 self.unroll_batch = new
 
@@ -580,12 +580,12 @@ class ConvOp(OpenMPOp):
                     new -= 1
 
                 warnstr = (
-                    "OPTIMISATION WARNING: in ConvOp.__init__()"
-                    " unroll_kern(%i) should be 0 or a divisor of"
-                    " nkern(%i). We revert it to %i. This"
+                    "In ConvOp.__init__(): "
+                    f"unroll_kern({self.unroll_kern}) must be 0 or a divisor of"
+                    f" nkern({self.nkern}). We revert it to {new}. This"
                     " won't change the result, but may make it slower."
                 )
-                _logger.warning(warnstr, self.unroll_kern, self.nkern, new)
+                _logger.warning(warnstr)
                 self.unroll_kern = new
 
         self.outshp = get_conv_output_shape(

theano/tensor/nnet/nnet.py

@@ -440,9 +440,10 @@ class Softmax(Op):
             raise ValueError(f"x must be 1-d or 2-d tensor of floats. Got {x.type}")
         if x.ndim == 1:
             warnings.warn(
-                "DEPRECATION: If x is a vector, Softmax will not automatically pad x "
+                "If x is a vector, Softmax will not automatically pad x "
                 "anymore in next releases. If you need it, please do it manually. The "
                 "vector case is gonna be supported soon and the output will be a vector.",
+                category=PendingDeprecationWarning,
                 stacklevel=4,
             )
             x = tt.shape_padleft(x, n_ones=1)
@@ -638,9 +639,10 @@ class LogSoftmax(Op):
             raise ValueError(f"x must be 1-d or 2-d tensor of floats. Got {x.type}")
         if x.ndim == 1:
             warnings.warn(
-                "DEPRECATION: If x is a vector, LogSoftmax will not automatically pad x "
+                "If x is a vector, LogSoftmax will not automatically pad x "
                 "anymore in next releases. If you need it, please do it manually. The "
                 "vector case is gonna be supported soon and the output will be a vector.",
+                category=PendingDeprecationWarning,
                 stacklevel=4,
             )
             x = tt.shape_padleft(x, n_ones=1)
@@ -1661,8 +1663,7 @@ def local_argmax_pushdown(fgraph, node):
     ):
         if theano.config.warn.argmax_pushdown_bug:
             logging.getLogger("theano.tensor.nnet.nnet").warn(
-                "WARNING: there "
-                "was a bug in Theano fixed on May 27th, 2010 in this case."
+                "There was a bug in Theano fixed on May 27th, 2010 in this case."
                 " I.E. when we take the max of a softplus, softmax, exp, "
                 "log, tanh, sigmoid, softmax_with_bias op, we were doing "
                 "the max of the parent of the input. To remove this "

theano/tensor/opt.py

@@ -5821,7 +5821,7 @@ def local_sum_prod_div_dimshuffle(fgraph, node):
 
                 if compatible_dims:
                     _logger.warning(
-                        "WARNING: Your current code is fine, but"
+                        "Your current code is fine, but"
                         " Theano versions between "
                         "rev. 3bd9b789f5e8 (2010-06-16) and"
                         " cfc6322e5ad4 (2010-08-03) would "
@@ -5876,7 +5876,7 @@ def local_sum_prod_div_dimshuffle(fgraph, node):
                             node.op, Sum
                         ):
                             _logger.warning(
-                                "WARNING: Your current code is fine,"
+                                "Your current code is fine,"
                                 " but Theano versions between "
                                 "rev. 3bd9b789f5e8 (2010-06-16) and"
                                 " cfc6322e5ad4 (2010-08-03) would "
@@ -5986,7 +5986,7 @@ def local_op_of_op(fgraph, node):
                     and len(newaxis) == len(newaxis_old)
                 ):
                     _logger.warning(
-                        "WARNING (YOUR CURRENT CODE IS FINE): Theano "
+                        "(YOUR CURRENT CODE IS FINE): Theano "
                         "versions between version 9923a40c7b7a and August "
                         "2nd, 2010 generated bugged code in this case. "
                         "This happens when there are two consecutive sums "

theano/tensor/raw_random.py

 """Define random number Type (`RandomStateType`) and Op (`RandomFunction`)."""
 
-
-import sys
 from copy import copy
 from functools import reduce
+from warnings import warn
 
 import numpy as np
 
@@ -200,10 +199,8 @@ class RandomFunction(gof.Op):
         assert shape.type.ndim == 1
         assert (shape.type.dtype == "int64") or (shape.type.dtype == "int32")
         if not isinstance(r.type, RandomStateType):
-            print(
-                "WARNING: RandomState instances should be in RandomStateType",
-                file=sys.stderr,
-            )
+            warn("RandomState instances should be in RandomStateType")
+
         # the following doesn't work because we want to ignore the
         # broadcastable flags in shape.type
         # assert shape.type == tensor.lvector
@@ -532,9 +529,9 @@ def binomial(random_state, size=None, n=1, p=0.5, ndim=None, dtype="int64", prob
     """
     if prob is not None:
         p = prob
-        print(
-            "DEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as np.",
-            file=sys.stderr,
+        warn(
+            "The parameter prob to the binomal fct have been renamed to p to have the same name as np.",
+            category=DeprecationWarning,
         )
     n = tensor.as_tensor_variable(n)
     p = tensor.as_tensor_variable(p)
@@ -964,9 +961,9 @@ class RandomStreamsBase:
         """
         if prob is not None:
             p = prob
-            print(
-                "DEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as numpy.",
-                file=sys.stderr,
+            warn(
+                "The parameter prob to the binomal fct have been renamed to p to have the same name as numpy.",
+                category=DeprecationWarning,
             )
         return self.gen(binomial, size, n, p, ndim=ndim, dtype=dtype)
 

theano/tensor/signal/pool.py

@@ -93,9 +93,10 @@ def pool_2d(
             )
         else:
             warnings.warn(
-                "DEPRECATION: the 'ds' parameter is not going to exist"
+                "The 'ds' parameter is not going to exist"
                 " anymore as it is going to be replaced by the parameter"
                 " 'ws'.",
+                category=DeprecationWarning,
                 stacklevel=2,
             )
             ws = ds
@@ -110,9 +111,10 @@ def pool_2d(
             )
         else:
             warnings.warn(
-                "DEPRECATION: the 'st' parameter is not going to exist"
+                "The 'st' parameter is not going to exist"
                 " anymore as it is going to be replaced by the parameter"
                 " 'stride'.",
+                category=DeprecationWarning,
                 stacklevel=2,
             )
             stride = st
@@ -125,9 +127,10 @@ def pool_2d(
             )
         else:
             warnings.warn(
-                "DEPRECATION: the 'padding' parameter is not going to exist"
+                "The 'padding' parameter is not going to exist"
                 " anymore as it is going to be replaced by the parameter"
                 " 'pad'.",
+                category=DeprecationWarning,
                 stacklevel=2,
             )
             pad = padding
@@ -145,6 +148,7 @@ def pool_2d(
             " GPU combination supported is when"
             " `ws == stride and pad == (0, 0) and mode == 'max'`."
             " Otherwise, the convolution will be executed on CPU.",
+            category=DeprecationWarning,
             stacklevel=2,
         )
         ignore_border = False
@@ -210,9 +214,10 @@ def pool_3d(
             )
         else:
             warnings.warn(
-                "DEPRECATION: the 'ds' parameter is not going to exist"
+                "The 'ds' parameter is not going to exist"
                 " anymore as it is going to be replaced by the parameter"
                 " 'ws'.",
+                category=DeprecationWarning,
                 stacklevel=2,
             )
             ws = ds
@@ -227,9 +232,10 @@ def pool_3d(
             )
         else:
             warnings.warn(
-                "DEPRECATION: the 'st' parameter is not going to exist"
+                "The 'st' parameter is not going to exist"
                 " anymore as it is going to be replaced by the parameter"
                 " 'stride'.",
+                category=DeprecationWarning,
                 stacklevel=2,
             )
             stride = st
@@ -242,9 +248,10 @@ def pool_3d(
             )
         else:
             warnings.warn(
-                "DEPRECATION: the 'padding' parameter is not going to exist"
+                "The 'padding' parameter is not going to exist"
                 " anymore as it is going to be replaced by the parameter"
                 " 'pad'.",
+                category=DeprecationWarning,
                 stacklevel=2,
             )
             pad = padding
@@ -262,6 +269,7 @@ def pool_3d(
             " GPU combination supported is when"
             " `ws == stride and pad == (0, 0, 0) and mode == 'max'`."
             " Otherwise, the convolution will be executed on CPU.",
+            category=DeprecationWarning,
             stacklevel=2,
         )
         ignore_border = False
@@ -387,9 +395,10 @@ class Pool(OpenMPOp):
                 )
             else:
                 warnings.warn(
-                    "DEPRECATION: the 'ds' parameter is not going to exist"
+                    "The 'ds' parameter is not going to exist"
                     " anymore as it is going to be replaced by the parameter"
                     " 'ws'.",
+                    category=DeprecationWarning,
                     stacklevel=2,
                 )
                 ws = ds
@@ -404,9 +413,10 @@ class Pool(OpenMPOp):
                 )
             else:
                 warnings.warn(
-                    "DEPRECATION: the 'st' parameter is not going to exist"
+                    "The 'st' parameter is not going to exist"
                     " anymore as it is going to be replaced by the parameter"
                     " 'stride'.",
+                    category=DeprecationWarning,
                     stacklevel=2,
                 )
                 stride = st
@@ -420,9 +430,10 @@ class Pool(OpenMPOp):
                 )
             else:
                 warnings.warn(
-                    "DEPRECATION: the 'padding' parameter is not going to"
+                    "The 'padding' parameter is not going to"
                     " exist anymore as it is going to be replaced by the"
                     " parameter 'pad'.",
+                    category=DeprecationWarning,
                     stacklevel=2,
                 )
                 pad = padding
@@ -1032,9 +1043,10 @@ class PoolGrad(OpenMPOp):
                 )
             else:
                 warnings.warn(
-                    "DEPRECATION: the 'ds' parameter in PoolGrad is not going"
+                    "The 'ds' parameter in PoolGrad is not going"
                     " to exist anymore as it is going to be replaced by the"
                     " parameter 'ws'.",
+                    category=DeprecationWarning,
                     stacklevel=2,
                 )
                 ws = ds
@@ -1049,9 +1061,10 @@ class PoolGrad(OpenMPOp):
                 )
             else:
                 warnings.warn(
-                    "DEPRECATION: the 'st' parameter in PoolGrad is not going"
+                    "The 'st' parameter in PoolGrad is not going"
                     " to exist anymore as it is going to be replaced by the"
                     " parameter 'stride'.",
+                    category=DeprecationWarning,
                     stacklevel=2,
                 )
                 stride = st
@@ -1064,9 +1077,10 @@ class PoolGrad(OpenMPOp):
                 )
             else:
                 warnings.warn(
-                    "DEPRECATION: the 'padding' parameter in PoolGrad is not"
+                    "The 'padding' parameter in PoolGrad is not"
                     " going to exist anymore as it is going to be replaced"
                     " by the parameter 'pad'.",
+                    category=DeprecationWarning,
                     stacklevel=2,
                 )
                 pad = padding

theano/tensor/type.py

@@ -55,9 +55,10 @@ class TensorType(Type):
         self.sparse_grad = sparse_grad
         if sparse_grad:
             warnings.warn(
-                "DEPRECATION WARNING: You use an old interface to"
+                "You use an old interface to"
                 " AdvancedSubtensor1 sparse_grad. Now use"
-                " theano.sparse_grad(a_tensor[an_int_vector])."
+                " theano.sparse_grad(a_tensor[an_int_vector]).",
+                category=DeprecationWarning,
             )
 
     def clone(self, dtype=None, broadcastable=None):