diff --git a/src/mygrad/operation_base.py b/src/mygrad/operation_base.py
index 3be681d1..655d3bc2 100644
--- a/src/mygrad/operation_base.py
+++ b/src/mygrad/operation_base.py
@@ -8,6 +8,7 @@
 
 import numpy as np
 
+from mygrad._numpy_version import NP_IS_V2
 from mygrad._utils import SkipGradient, reduce_broadcast
 from mygrad.errors import InvalidBackprop, InvalidGradient
 from mygrad.typing import DTypeLike, Mask
@@ -97,7 +98,10 @@ def grad_post_process_fn(
 
         if out.ndim == 0:
             # sum-reduction to a scalar produces a float
-            out = np.asarray(out)
+            if NP_IS_V2:
+                out = np.asarray(out)
+            else:  # pragma: no cover
+                np.array(out, copy=False)
         return out
 
     @abstractmethod
@@ -198,7 +202,10 @@ def backward(
                     f"numpy arrays, got a gradient of type: {type(backed_grad)}"
                 )
 
-            backed_grad = np.asarray(backed_grad)
+            if NP_IS_V2:
+                backed_grad = np.asarray(backed_grad)
+            else:  # pragma: no cover
+                np.array(backed_grad, copy=False)
 
             if self.where is not True:
                 backed_grad = backed_grad * self.where
diff --git a/src/mygrad/tensor_base.py b/src/mygrad/tensor_base.py
index e06095f3..d3d8cbe7 100644
--- a/src/mygrad/tensor_base.py
+++ b/src/mygrad/tensor_base.py
@@ -29,6 +29,7 @@
 import mygrad._utils.duplicating_graph as _dup
 import mygrad._utils.graph_tracking as _track
 import mygrad._utils.lock_management as _mem
+from mygrad._numpy_version import NP_IS_V2
 from mygrad._tensor_core_ops.indexing import GetItem, SetItem
 from mygrad._utils import WeakRef, WeakRefIterable, collect_all_tensors_and_clear_grads
 from mygrad.errors import DisconnectedView
@@ -738,7 +739,12 @@ def __array_function__(
     def __array__(
         self, dtype: DTypeLike = None, copy: Optional[bool] = None
     ) -> np.ndarray:
-        return np.asarray(self.data, dtype=dtype, copy=copy)
+        if NP_IS_V2:
+            return np.asarray(self.data, dtype=dtype, copy=copy)
+        else:  # pragma: no cover
+            if copy is None:
+                copy = False
+            return np.array(self.data, dtype=dtype, copy=copy)
 
     def __init__(
         self,
@@ -798,18 +804,21 @@ def __init__(
 
         self._creator: Optional[Operation] = _creator
 
-        if copy is False:
-            self.data = np.asarray(x, dtype=dtype)  # type: np.ndarray
-            if not isinstance(ndmin, Integral):
-                raise TypeError(
-                    f"'{type(ndmin)}' object cannot be interpreted as an integer"
-                )
-            if ndmin and self.data.ndim < ndmin:
-                self.data = self.data[(*(None for _ in range(ndmin - self.data.ndim)),)]
+        if not NP_IS_V2:
+            self.data = np.array(x, dtype=dtype, copy=copy, ndmin=ndmin)
         else:
-            self.data = np.array(
-                x, dtype=dtype, copy=copy, ndmin=ndmin
-            )  # type: np.ndarray
+            if copy is False:
+                self.data = np.asarray(x, dtype=dtype)
+                if not isinstance(ndmin, Integral):
+                    raise TypeError(
+                        f"'{type(ndmin)}' object cannot be interpreted as an integer"
+                    )
+                if ndmin and self.data.ndim < ndmin:
+                    self.data = self.data[
+                        (*(None for _ in range(ndmin - self.data.ndim)),)
+                    ]
+            else:
+                self.data = np.array(x, dtype=dtype, copy=copy, ndmin=ndmin)
 
         dtype = self.data.dtype.type
         is_float = issubclass(dtype, np.floating)  # faster than `numpy.issubdtype`