[GraphBolt] remove duplicate test utils (#6220)

tests/python/pytorch/graphbolt/impl/gb_test_utils.py

-import dgl.graphbolt as gb
-import scipy.sparse as sp
-import torch
-
-
-def rand_csc_graph(N, density):
-    adj = sp.random(N, N, density)
-    adj = adj + adj.T
-    adj = adj.tocsc()
-
-    indptr = torch.LongTensor(adj.indptr)
-    indices = torch.LongTensor(adj.indices)
-
-    graph = gb.from_csc(indptr, indices)
-
-    return graph
-
-
-def random_homo_graph(num_nodes, num_edges):
-    csc_indptr = torch.randint(0, num_edges, (num_nodes + 1,))
-    csc_indptr = torch.sort(csc_indptr)[0]
-    csc_indptr[0] = 0
-    csc_indptr[-1] = num_edges
-    indices = torch.randint(0, num_nodes, (num_edges,))
-    return csc_indptr, indices
-
-
-def get_metadata(num_ntypes, num_etypes):
-    ntypes = {f"n{i}": i for i in range(num_ntypes)}
-    etypes = {}
-    count = 0
-    for n1 in range(num_ntypes):
-        for n2 in range(n1, num_ntypes):
-            if count >= num_etypes:
-                break
-            etypes.update({(f"n{n1}", f"e{count}", f"n{n2}"): count})
-            count += 1
-    return gb.GraphMetadata(ntypes, etypes)
-
-
-def random_hetero_graph(num_nodes, num_edges, num_ntypes, num_etypes):
-    csc_indptr, indices = random_homo_graph(num_nodes, num_edges)
-    metadata = get_metadata(num_ntypes, num_etypes)
-    # Randomly get node type split point.
-    node_type_offset = torch.sort(
-        torch.randint(0, num_nodes, (num_ntypes + 1,))
-    )[0]
-    node_type_offset[0] = 0
-    node_type_offset[-1] = num_nodes
-
-    type_per_edge = []
-    for i in range(num_nodes):
-        num = csc_indptr[i + 1] - csc_indptr[i]
-        type_per_edge.append(
-            torch.sort(torch.randint(0, num_etypes, (num,)))[0]
-        )
-    type_per_edge = torch.cat(type_per_edge, dim=0)
-    return (csc_indptr, indices, node_type_offset, type_per_edge, metadata)