diff --git a/einops/experimental/data_api_packing.py b/einops/experimental/data_api_packing.py
deleted file mode 100644
index 5e3e04c5..00000000
--- a/einops/experimental/data_api_packing.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from typing import List, TypeVar, Tuple, Sequence
-
-from einops import EinopsError
-
-T = TypeVar('T')
-
-Shape = Tuple[int, ...]
-
-
-def pack(pattern: str, tensors: Sequence[T]) -> Tuple[T, List[Shape]]:
-    axes = pattern.split()
-    if len(axes) != len(set(axes)):
-        raise EinopsError(f'Duplicates in axes names in pack("{pattern}", ...)')
-    if '*' not in axes:
-        raise EinopsError(f'No *-axis in pack("{pattern}", ...)')
-
-    # need some validation of identifiers
-
-    n_axes_before = axes.index('*')
-    n_axes_after = len(axes) - n_axes_before - 1
-    min_axes = n_axes_before + n_axes_after
-
-    xp = tensors[0].__array_namespace__()
-
-    reshaped_tensors: List[T] = []
-    packed_shapes: List[Shape] = []
-    for i, tensor in enumerate(tensors):
-        shape = tensor.shape
-        if len(shape) < min_axes:
-            raise EinopsError(f'packed tensor #{i} (enumeration starts with 0) has shape {shape}, '
-                              f'while pattern {pattern} assumes at least {min_axes} axes')
-        axis_after_packed_axes = len(shape) - n_axes_after
-        packed_shapes.append(shape[n_axes_before:])
-        reshaped_tensors.append(
-            xp.reshape(tensor, (*shape[:n_axes_before], -1, *shape[axis_after_packed_axes:]))
-        )
-
-    return xp.concat(reshaped_tensors, axis=n_axes_before), packed_shapes
-
-
-def prod(x: Shape) -> int:
-    result = 1
-    for i in x:
-        result *= i
-    return result
-
-
-def unpack(pattern: str, tensor: T, packed_shapes: List[Shape]) -> List[T]:
-    axes = pattern.split()
-    if len(axes) != len(set(axes)):
-        raise EinopsError(f'Duplicates in axes names in unpack("{pattern}", ...)')
-    if '*' not in axes:
-        raise EinopsError(f'No *-axis in unpack("{pattern}", ...)')
-
-    # need some validation of identifiers
-
-    input_shape = tensor.shape
-    if len(input_shape) != len(axes):
-        raise EinopsError(f'unpack({pattern}, ...) received input of wrong dim with shape {input_shape}')
-
-    unpacked_axis = axes.index('*')
-
-    lengths_of_composed_axes: List[int] = [
-        -1 if -1 in p_shape else prod(p_shape)
-        for p_shape in packed_shapes
-    ]
-
-    n_unknown_composed_axes = sum(x == -1 for x in lengths_of_composed_axes)
-    if n_unknown_composed_axes > 1:
-        raise EinopsError(
-            f"unpack({pattern}, ...) received more than one -1 in {packed_shapes} and can't infer dimensions"
-        )
-
-    # following manipulations allow to skip some shape verifications
-    # and leave them to backends
-
-    # [[], [2, 3], [4], [-1, 5], [6]] < examples of packed_axis
-    # split positions when computed should be
-    # [0,   1,      7,   11,      N-6 , N ], where N = length of axis
-    split_positions = [0] * len(packed_shapes) + [input_shape[unpacked_axis]]
-    if n_unknown_composed_axes == 0:
-        for i, x in enumerate(lengths_of_composed_axes[:-1]):
-            split_positions[i + 1] = split_positions[i] + x
-    else:
-        unknown_composed_axis: int = lengths_of_composed_axes.index(-1)
-        for i in range(unknown_composed_axis):
-            split_positions[i + 1] = split_positions[i] + lengths_of_composed_axes[i]
-        for j in range(unknown_composed_axis + 1, len(lengths_of_composed_axes))[::-1]:
-            split_positions[j] = split_positions[j + 1] + lengths_of_composed_axes[j]
-
-    xp = tensor.__array_namespace__()
-    shape_start = input_shape[:unpacked_axis]
-    shape_end = input_shape[unpacked_axis + 1:]
-    slice_filler = (slice(None, None),) * unpacked_axis
-    return [
-        xp.reshape(
-            # shortest way slice arbitrary axis
-            tensor[(*slice_filler, slice(split_positions[i], split_positions[i + 1]))],
-            (*shape_start, *element_shape, *shape_end)
-        )
-        for i, element_shape in enumerate(packed_shapes)
-    ]
-
-
-if __name__ == '__main__':
-    import numpy.array_api as np
-
-    H = 100
-    W = 101
-    C = 3
-
-    r = np.zeros((H, W))
-    g = np.zeros((H, W))
-    b = np.zeros((H, W))
-    embeddings = np.zeros((H, W, 32))
-
-    im = np.stack([r, g, b], axis=-1)
-    print(im.shape)
-
-    image, shapes = pack('h w *', [r, g, b])
-    print(image.shape, shapes)
-
-    print(type(image))
-    print(type(im))
-    assert np.all(np.equal(image, im))
-
-    images_and_embedding, shapes = pack('h w *', [r, g, b, embeddings])
-    print(images_and_embedding.shape, shapes)
-    r2, g2, b2, embeddings2 = unpack('h w *', images_and_embedding, shapes)
-    assert np.all(np.equal(r, r2))
-    assert np.all(np.equal(g, g2))
-    assert np.all(np.equal(b, b2))
-    assert np.all(np.equal(embeddings, embeddings2))
-
-    print([x.shape for x in unpack('h w *', images_and_embedding, shapes[1:])])
-
-    print('all is fine')