using for loop to install conda package

.CondaPkg/env/Lib/site-packages/numpy/lib/arrayterator.pyi

@@ -0,0 +1,49 @@
+from collections.abc import Generator
+from typing import (
+    Any,
+    TypeVar,
+    Union,
+    overload,
+)
+
+from numpy import ndarray, dtype, generic
+from numpy._typing import DTypeLike
+
+# TODO: Set a shape bound once we've got proper shape support
+_Shape = TypeVar("_Shape", bound=Any)
+_DType = TypeVar("_DType", bound=dtype[Any])
+_ScalarType = TypeVar("_ScalarType", bound=generic)
+
+_Index = Union[
+    Union[ellipsis, int, slice],
+    tuple[Union[ellipsis, int, slice], ...],
+]
+
+__all__: list[str]
+
+# NOTE: In reality `Arrayterator` does not actually inherit from `ndarray`,
+# but its ``__getattr__` method does wrap around the former and thus has
+# access to all its methods
+
+class Arrayterator(ndarray[_Shape, _DType]):
+    var: ndarray[_Shape, _DType]  # type: ignore[assignment]
+    buf_size: None | int
+    start: list[int]
+    stop: list[int]
+    step: list[int]
+
+    @property  # type: ignore[misc]
+    def shape(self) -> tuple[int, ...]: ...
+    @property
+    def flat(  # type: ignore[override]
+        self: ndarray[Any, dtype[_ScalarType]]
+    ) -> Generator[_ScalarType, None, None]: ...
+    def __init__(
+        self, var: ndarray[_Shape, _DType], buf_size: None | int = ...
+    ) -> None: ...
+    @overload
+    def __array__(self, dtype: None = ...) -> ndarray[Any, _DType]: ...
+    @overload
+    def __array__(self, dtype: DTypeLike) -> ndarray[Any, dtype[Any]]: ...
+    def __getitem__(self, index: _Index) -> Arrayterator[Any, _DType]: ...
+    def __iter__(self) -> Generator[ndarray[Any, _DType], None, None]: ...