Improve docstring for explicit_graph_inputs

Author: Dhruvanshu-Joshi

pytensor/graph/basic.py

@@ -936,6 +936,56 @@ def graph_inputs(
     yield from (r for r in ancestors(graphs, blockers) if r.owner is None)
 
 
+def explicit_graph_inputs(
+    graph: Variable[Any, Any] | Iterable[Variable[Any, Any]],
+) -> Generator[Variable, None, None]:
+    """
+    Get the root variables needed as inputs to a function that computes `graph`
+
+    Parameters
+    ----------
+    graph : TensorVariable
+        Output `Variable` instances for which to search backward through
+        owners.
+
+    Returns
+    -------
+    iterable
+        Generator of root Variables (without owner) needed to compile a function that evaluates `graphs`.
+
+    Examples
+    --------
+
+    .. code-block:: python
+
+        import pytensor
+        import numpy as np
+        import pytensor.tensor as pt
+        from pytensor.graph.basic import explicit_graph_inputs
+
+        x = pt.vector('x')
+        y = pt.constant(2)
+        z = pt.mul(x*y)
+
+        out = list(explicit_graph_inputs(z))
+        f = pytensor.function([x], out)
+        eval = f(np.array([1, 2, 3]))
+
+        print(eval)
+        # [array([1., 2., 3.])]
+    """
+    from pytensor.compile.sharedvalue import SharedVariable
+
+    if isinstance(graph, Variable):
+        graph = [graph]
+
+    return (
+        v
+        for v in graph_inputs(graph)
+        if isinstance(v, Variable) and not isinstance(v, Constant | SharedVariable)
+    )
+
+
 def vars_between(
     ins: Collection[Variable], outs: Iterable[Variable]
 ) -> Generator[Variable, None, None]:

tests/graph/test_basic.py

@@ -18,6 +18,7 @@
     clone,
     clone_get_equiv,
     equal_computations,
+    explicit_graph_inputs,
     general_toposort,
     get_var_by_name,
     graph_inputs,
@@ -522,6 +523,19 @@ def test_graph_inputs():
     assert res_list == [r3, r1, r2]
 
 
+def test_explicit_graph_inputs():
+    x = pt.fscalar()
+    y = pt.constant(2)
+    z = shared(1)
+    a = pt.sum(x + y + z)
+    b = pt.true_div(x, y)
+
+    res = list(explicit_graph_inputs([a]))
+    res1 = list(explicit_graph_inputs(b))
+
+    assert res, res1 == [x]
+
+
 def test_variables_and_orphans():
     r1, r2, r3 = MyVariable(1), MyVariable(2), MyVariable(3)
     o1 = MyOp(r1, r2)