Remove SciPy check from aesara.sparse

aesara/sparse/__init__.py

-from warnings import warn
-
-
-try:
-    import scipy
-
-    enable_sparse = True
-except ImportError:
-    enable_sparse = False
-    warn("SciPy can't be imported.  Sparse matrix support is disabled.")
-
+from aesara.sparse import rewriting, sharedvar
+from aesara.sparse.basic import *
+from aesara.sparse.sharedvar import sparse_constructor as shared
 from aesara.sparse.type import SparseTensorType, _is_sparse
 
 
-if enable_sparse:
-    from aesara.sparse import rewriting, sharedvar
-    from aesara.sparse.basic import *
-    from aesara.sparse.sharedvar import sparse_constructor as shared
-
-    def sparse_grad(var):
-        """This function return a new variable whose gradient will be
-        stored in a sparse format instead of dense.
+def sparse_grad(var):
+    """This function return a new variable whose gradient will be
+    stored in a sparse format instead of dense.
 
-        Currently only variable created by AdvancedSubtensor1 is supported.
-        i.e. a_tensor_var[an_int_vector].
+    Currently only variable created by AdvancedSubtensor1 is supported.
+    i.e. a_tensor_var[an_int_vector].
 
-        .. versionadded:: 0.6rc4
-        """
-        from aesara.tensor.subtensor import AdvancedSubtensor, AdvancedSubtensor1
+    .. versionadded:: 0.6rc4
+    """
+    from aesara.tensor.subtensor import AdvancedSubtensor, AdvancedSubtensor1
 
-        if var.owner is None or not isinstance(
-            var.owner.op, (AdvancedSubtensor, AdvancedSubtensor1)
-        ):
-            raise TypeError(
-                "Sparse gradient is only implemented for AdvancedSubtensor and AdvancedSubtensor1"
-            )
+    if var.owner is None or not isinstance(
+        var.owner.op, (AdvancedSubtensor, AdvancedSubtensor1)
+    ):
+        raise TypeError(
+            "Sparse gradient is only implemented for AdvancedSubtensor and AdvancedSubtensor1"
+        )
 
-        x = var.owner.inputs[0]
-        indices = var.owner.inputs[1:]
+    x = var.owner.inputs[0]
+    indices = var.owner.inputs[1:]
 
-        if len(indices) > 1:
-            raise TypeError(
-                "Sparse gradient is only implemented for single advanced indexing"
-            )
+    if len(indices) > 1:
+        raise TypeError(
+            "Sparse gradient is only implemented for single advanced indexing"
+        )
 
-        ret = AdvancedSubtensor1(sparse_grad=True)(x, indices[0])
-        return ret
+    ret = AdvancedSubtensor1(sparse_grad=True)(x, indices[0])
+    return ret