diff --git a/docs/changelog.md b/docs/changelog.md
index 938d9bd..683372e 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -2,6 +2,61 @@
 
 ## 1.*
 
+### 1.3.3 - 24-08-13 - Callable type annotations
+
+Problem, when you use a numpydantic `"wrap"` validator, it gives the annotation as a `handler` function. 
+
+So this is effectively what happens
+
+```python
+@field_validator("*", mode="wrap")
+@classmethod
+def cast_specified_columns(
+    cls, val: Any, handler: ValidatorFunctionWrapHandler, info: ValidationInfo
+) -> Any:
+    # where handler is the callable here
+    # so 
+    # return handler(val)
+    
+    return NDArray[Any, Any](val)
+```
+
+where `Any, Any` is whatever you had put in there. 
+
+So this makes it so you can use an annotation as a functional validator. it looks a little bit whacky but idk it makes sense as a PARAMETERIZED TYPE
+
+```python
+>>> from numpydantic import NDArray, Shape
+>>> import numpy as np
+
+>>> array = np.array([1,2,3], dtype=int)
+>>> validated = NDArray[Shape["3"], int](array)
+>>> assert validated is array
+True
+
+>>> bad_array = np.array([1,2,3,4], dtype=int)
+>>> _ = NDArray[Shape["3"], int](bad_array)
+    175 """
+    176 Raise a ShapeError if the shape is invalid.
+    177 
+    178 Raises:
+    179     :class:`~numpydantic.exceptions.ShapeError`
+    180 """
+    181 if not valid:
+--> 182     raise ShapeError(
+    183         f"Invalid shape! expected shape {self.shape.prepared_args}, "
+    184         f"got shape {shape}"
+    185     )
+
+ShapeError: Invalid shape! expected shape ['3'], got shape (4,)
+
+```
+
+**Performance:**
+- Don't import the pandas module if we don't have to, since we are not
+  using it. This shaves ~600ms off import time.
+
+
 ### 1.3.2 - 24-08-12 - Allow subclasses of dtypes
 
 (also when using objects for dtypes, subclasses of that object are allowed to validate)
diff --git a/pyproject.toml b/pyproject.toml
index e1ffa3d..8cdc372 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "numpydantic"
-version = "1.3.2"
+version = "1.3.3"
 description = "Type and shape validation and serialization for numpy arrays in pydantic models"
 authors = [
     {name = "sneakers-the-rat", email = "sneakers-the-rat@protonmail.com"},
diff --git a/src/numpydantic/ndarray.py b/src/numpydantic/ndarray.py
index 8756ae0..d951d3a 100644
--- a/src/numpydantic/ndarray.py
+++ b/src/numpydantic/ndarray.py
@@ -29,7 +29,7 @@ from numpydantic.schema import (
     get_validate_interface,
     make_json_schema,
 )
-from numpydantic.types import DtypeType, ShapeType
+from numpydantic.types import DtypeType, NDArrayType, ShapeType
 from numpydantic.vendor.nptyping.error import InvalidArgumentsError
 from numpydantic.vendor.nptyping.ndarray import NDArrayMeta as _NDArrayMeta
 from numpydantic.vendor.nptyping.nptyping_type import NPTypingType
@@ -54,6 +54,10 @@ class NDArrayMeta(_NDArrayMeta, implementation="NDArray"):
     if TYPE_CHECKING:  # pragma: no cover
         __getitem__ = SubscriptableMeta.__getitem__
 
+    def __call__(cls, val: NDArrayType) -> NDArrayType:
+        """Call ndarray as a validator function"""
+        return get_validate_interface(cls.__args__[0], cls.__args__[1])(val)
+
     def __instancecheck__(self, instance: Any):
         """
         Extended type checking that determines whether
diff --git a/src/numpydantic/vendor/nptyping/__init__.py b/src/numpydantic/vendor/nptyping/__init__.py
index 4ca8fdb..0a51854 100644
--- a/src/numpydantic/vendor/nptyping/__init__.py
+++ b/src/numpydantic/vendor/nptyping/__init__.py
@@ -32,7 +32,9 @@ from numpydantic.vendor.nptyping.error import (
 )
 from numpydantic.vendor.nptyping.ndarray import NDArray
 from numpydantic.vendor.nptyping.package_info import __version__
-from numpydantic.vendor.nptyping.pandas_.dataframe import DataFrame
+
+# don't import unnecessarily since we don't use it
+# from numpydantic.vendor.nptyping.pandas_.dataframe import DataFrame
 from numpydantic.vendor.nptyping.recarray import RecArray
 from numpydantic.vendor.nptyping.shape import Shape
 from numpydantic.vendor.nptyping.shape_expression import (
diff --git a/src/numpydantic/vendor/nptyping/ndarray.py b/src/numpydantic/vendor/nptyping/ndarray.py
index 90a4793..19f06b4 100644
--- a/src/numpydantic/vendor/nptyping/ndarray.py
+++ b/src/numpydantic/vendor/nptyping/ndarray.py
@@ -31,7 +31,6 @@ import numpy as np
 from numpydantic.vendor.nptyping.base_meta_classes import (
     FinalMeta,
     ImmutableMeta,
-    InconstructableMeta,
     MaybeCheckableMeta,
     PrintableMeta,
     SubscriptableMeta,
@@ -54,7 +53,6 @@ from numpydantic.vendor.nptyping.typing_ import (
 
 class NDArrayMeta(
     SubscriptableMeta,
-    InconstructableMeta,
     ImmutableMeta,
     FinalMeta,
     MaybeCheckableMeta,
diff --git a/tests/test_ndarray.py b/tests/test_ndarray.py
index 9883c2a..f92a66d 100644
--- a/tests/test_ndarray.py
+++ b/tests/test_ndarray.py
@@ -350,3 +350,17 @@ def test_instancecheck():
         return array
 
     my_function(np.zeros((1, 2, 3), int))
+
+
+def test_callable():
+    """
+    NDArray objects are callable to validate and cast
+    Don't test validation here, just that we can be called
+    """
+    annotation = NDArray[Shape["3"], int]
+    array = np.array([1, 2, 3], dtype=int)
+    validated = annotation(array)
+    assert validated is array
+
+    with pytest.raises(DtypeError):
+        _ = annotation(np.zeros((1, 2, 3)))