<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def velocity_graph(data, vkey='velocity', xkey='Ms', tkey=None, basis=None, n_neighbors=None, n_recurse_neighbors=None, random_neighbors_at_max=None, sqrt_transform=False, approx=False, copy=False):
 """Computes velocity graph based on cosine similarities. The cosine similarities are computed between velocities and potential cell state transitions. Arguments --------- data: :class:`~anndata.AnnData` Annotated data matrix. vkey: `str` (default: `'velocity'`) Name of velocity estimates to be used. n_neighbors: `int` or `None` (default: None) Use fixed number of neighbors or do recursive neighbor search (if `None`). n_recurse_neighbors: `int` (default: 2) Number of recursions to be done for neighbors search. random_neighbors_at_max: `int` or `None` (default: `None`) If number of iterative neighbors for an individual cell is higher than this threshold, a random selection of such are chosen as reference neighbors. sqrt_transform: `bool` (default: `False`) Whether to variance-transform the cell states changes and velocities before computing cosine similarities. copy: `bool` (default: `False`) Return a copy instead of writing to adata. Returns ------- Returns or updates `adata` with the attributes velocity_graph: `.uns` sparse matrix with transition probabilities """

    adata = data.copy() if copy else data
    if vkey not in adata.layers.keys(): velocity(adata, vkey=vkey)

    vgraph = VelocityGraph(adata, vkey=vkey, xkey=xkey, tkey=tkey, basis=basis, n_neighbors=n_neighbors, approx=approx,
                           n_recurse_neighbors=n_recurse_neighbors, random_neighbors_at_max=random_neighbors_at_max,
                           sqrt_transform=sqrt_transform, report=True)

    logg.info('computing velocity graph', r=True)
    vgraph.compute_cosines()

    adata.uns[vkey+'_graph'] = vgraph.graph
    adata.uns[vkey+'_graph_neg'] = vgraph.graph_neg

    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
    logg.hint(
        'added \n'
        '    \'' + vkey + '_graph\', sparse matrix with cosine correlations (adata.uns)')

    return adata if copy else None