chore: fix backend tests

keras_core/backend/numpy/core.py

@@ -8,7 +8,20 @@
 from keras_core.backend.common.keras_tensor import KerasTensor
 from keras_core.backend.common.stateless_scope import StatelessScope
 
-DYNAMIC_SHAPES_OK = False
+DYNAMIC_SHAPES_OK = True
+
+NUMPY_DTYPES = {
+    "float16": np.float16,
+    "float32": np.float32,
+    "float64": np.float64,
+    "uint8": np.uint8,
+    "uint16": np.uint16,
+    "uint32": np.uint32,
+    "int8": np.int8,
+    "int16": np.int16,
+    "int32": np.int32,
+    "int64": np.int64,
+}
 
 
 class Variable(KerasVariable):
@@ -75,19 +88,57 @@ def vectorized_map(function, elements):
 def compute_output_spec(fn, *args, **kwargs):
     with StatelessScope():
 
-        def convert_keras_tensor_to_numpy(x):
+        def has_none_shape(x):
             if isinstance(x, KerasTensor):
-                return np.ones(x.shape, dtype=x.dtype)
+                return None in x.shape
+            return False
+
+        none_in_shape = any(map(has_none_shape, nest.flatten((args, kwargs))))
+
+        def convert_keras_tensor_to_numpy(x, fill_value=None):
+            if isinstance(x, KerasTensor):
+                shape = list(x.shape)
+                if fill_value:
+                    for i, e in enumerate(shape):
+                        if e is None:
+                            shape[i] = fill_value
+                return np.empty(
+                    shape=shape,
+                    dtype=NUMPY_DTYPES[x.dtype],
+                )
             return x
 
-        args, kwargs = nest.map_structure(
-            convert_keras_tensor_to_numpy, (args, kwargs)
+        args_1, kwargs_1 = nest.map_structure(
+            lambda x: convert_keras_tensor_to_numpy(x, fill_value=83),
+            (args, kwargs),
         )
-        np_out = fn(*args, **kwargs)
+        outputs_1 = fn(*args_1, **kwargs_1)
+
+        outputs = outputs_1
+
+        if none_in_shape:
+            args_2, kwargs_2 = nest.map_structure(
+                lambda x: convert_keras_tensor_to_numpy(x, fill_value=89),
+                (args, kwargs),
+            )
+            outputs_2 = fn(*args_2, **kwargs_2)
+
+            flat_out_1 = nest.flatten(outputs_1)
+            flat_out_2 = nest.flatten(outputs_2)
+
+            flat_out = []
+            for x1, x2 in zip(flat_out_1, flat_out_2):
+                shape = list(x1.shape)
+                for i, e in enumerate(x2.shape):
+                    if e != shape[i]:
+                        shape[i] = None
+                flat_out.append(KerasTensor(shape, standardize_dtype(x1.dtype)))
+            outputs = nest.pack_sequence_as(outputs_1, flat_out)
 
         def convert_numpy_to_keras_tensor(x):
-            if isinstance(x, np.ndarray):
-                return KerasTensor(x.shape, x.dtype)
+            if is_tensor(x):
+                return KerasTensor(x.shape, standardize_dtype(x.dtype))
             return x
 
-        return nest.map_structure(convert_numpy_to_keras_tensor, np_out)
+        output_spec = nest.map_structure(convert_numpy_to_keras_tensor, outputs)
+    return output_spec