Implement SparseFromDense in numba backend

pytensor/link/numba/dispatch/sparse/basic.py

 import numpy as np
 import scipy as sp
+from numba.core import types
 from numba.extending import overload
 
 from pytensor import config
@@ -9,8 +10,19 @@ from pytensor.link.numba.dispatch.basic import (
     register_funcify_default_op_cache_key,
 )
 from pytensor.link.numba.dispatch.compile_ops import numba_deepcopy
-from pytensor.link.numba.dispatch.sparse.variable import CSMatrixType
-from pytensor.sparse import CSM, Cast, CSMProperties, DenseFromSparse, Transpose
+from pytensor.link.numba.dispatch.sparse.variable import (
+    CSMatrixType,
+    csc_matrix_from_components,
+    csr_matrix_from_components,
+)
+from pytensor.sparse import (
+    CSM,
+    Cast,
+    CSMProperties,
+    DenseFromSparse,
+    SparseFromDense,
+    Transpose,
+)
 
 
 @overload(numba_deepcopy)
@@ -84,3 +96,23 @@ def numba_funcify_DenseFromSparse(op, node, **kwargs):
         return x.toarray()
 
     return to_array
+
+
+@register_funcify_default_op_cache_key(SparseFromDense)
+def numba_funcify_SparseFromDense(op, node, **kwargs):
+    sparse_format = op.format
+
+    if sparse_format == "csr":
+
+        @numba_basic.numba_njit
+        def dense_to_csr(matrix):
+            return sp.sparse.csr_matrix(matrix)
+
+        return dense_to_csr
+    else:
+
+        @numba_basic.numba_njit
+        def dense_to_csc(matrix):
+            return sp.sparse.csc_matrix(matrix)
+
+        return dense_to_csc