def make_contiguous_numpy(tensor: TensorMap) -> TensorMap:
    """
    Takes a TensorMap of numpy backend and makes the ndarray block and
    gradient values contiguous in memory.
    """
    new_blocks = []
    for key, block in tensor.items():
        new_block = TensorBlock(
            values=np.ascontiguousarray(block.values),
            samples=block.samples,
            components=block.components,
            properties=block.properties,
        )
        for parameter, gradient in block.gradients():
            new_gradient = TensorBlock(
                values=np.ascontiguousarray(gradient.values),
                samples=gradient.samples,
                components=gradient.components,
                properties=gradient.properties,
            )
            new_block.add_gradient(parameter, new_gradient)
        new_blocks.append(new_block)

    return TensorMap(
        keys=tensor.keys,
        blocks=new_blocks,
    )