After some discussion on the PR [https://github.com/apache/arrow/pull/37166] we've altered the proposed type by removing the ndim parameter and adding ragged_dimensions one. If there is no further feedback I'd like to call for a vote early next week. Proposed language now reads:
Variable shape tensor ===================== * Extension name: `arrow.variable_shape_tensor`. * The storage type of the extension is: ``StructArray`` where struct is composed of **data** and **shape** fields describing a single tensor per row: * **data** is a ``List`` holding tensor elements of a single tensor. Data type of the list elements is uniform across the entire column and also provided in metadata. * **shape** is a ``FixedSizeList<uint32>[ndim]`` of the tensor shape where the size of the list ``ndim`` is equal to the number of dimensions of the tensor. * Extension type parameters: * **value_type** = the Arrow data type of individual tensor elements. Optional parameters describing the logical layout: * **dim_names** = explicit names to tensor dimensions as an array. The length of it should be equal to the shape length and equal to the number of dimensions. ``dim_names`` can be used if the dimensions have well-known names and they map to the physical layout (row-major). * **permutation** = indices of the desired ordering of the original dimensions, defined as an array. The indices contain a permutation of the values [0, 1, .., N-1] where N is the number of dimensions. The permutation indicates which dimension of the logical layout corresponds to which dimension of the physical tensor (the i-th dimension of the logical view corresponds to the dimension with number ``permutations[i]`` of the physical tensor). Permutation can be useful in case the logical order of the tensor is a permutation of the physical order (row-major). When logical and physical layout are equal, the permutation will always be ([0, 1, .., N-1]) and can therefore be left out. * **ragged_dimensions** = indices of ragged dimensions whose sizes may differ. Dimensions where all elements have the same size are called uniform dimensions. Indices are a subset of all possible dimension indices ([0, 1, .., N-1]). Ragged dimensions list can be left out. In that case all dimensions are assumed ragged. * Description of the serialization: The metadata must be a valid JSON object including number of dimensions of the contained tensors as an integer with key **"ndim"** plus optional dimension names with keys **"dim_names"** and ordering of the dimensions with key **"permutation"**. - Example with ``dim_names`` metadata for NCHW ordered data: ``{ "dim_names": ["C", "H", "W"] }`` - Example with ``ragged_dimensions`` metadata for a set of color images with variable width: ``{ "dim_names": ["H", "W", "C"], "ragged_dimensions": [1] }`` - Example of permuted 3-dimensional tensor: ``{ "permutation": [2, 0, 1] }`` This is the physical layout shape and the shape of the logical layout would given an individual tensor of shape [100, 200, 500] be ``[500, 100, 200]``. .. note:: Elements in a variable shape tensor extension array are stored in row-major/C-contiguous order. Rok