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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def tensor_space(shape, dtype=None, impl='numpy', **kwargs):
 """Return a tensor space with arbitrary scalar data type. Parameters shape : positive int or sequence of positive ints Number of entries per axis for elements in this space. A single integer results in a space with 1 axis. dtype : optional Data type of each element. Can be provided in any way the `numpy.dtype` function understands, e.g. as built-in type or as a string. For ``None``, the `TensorSpace.default_dtype` of the created space is used. impl : str, optional Impmlementation back-end for the space. See `odl.space.entry_points.tensor_space_impl_names` for available options. kwargs : Extra keyword arguments passed to the space constructor. Returns ------- space : `TensorSpace` Examples -------- Space of 3-tuples with ``uint64`` entries (although not strictly a vector space):
 tensor_space(3, dtype='uint64') 2x3 tensors with same data type: tensor_space((2, 3), dtype='uint64') The default data type depends on the implementation. For ``impl='numpy'``, it is ``'float64'``: rn((2, 3)) dtype('float64') See Also -------- rn, cn : Constructors for real and complex spaces """

    tspace_cls = tensor_space_impl(impl)

    if dtype is None:
        dtype = tspace_cls.default_dtype()

    # Use args by keyword since the constructor may take other arguments
    # by position
    return tspace_cls(shape=shape, dtype=dtype, **kwargs)