---

name: tensorflow-ops description: "Operate on Graph Nets GraphsTuple objects backed by TensorFlow tensors." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TensorFlow GraphsTuple operations

Use this sub-skill when the task already has graph data in Graph Nets GraphsTuple form, or has data dicts/NetworkX samples that must become TensorFlow-backed GraphsTuple values for batching, slicing, padding, tf.function, or TF1 session feeding.

Route here for

• Building TensorFlow GraphsTuples with utils_tf.data_dicts_to_graphs_tuple.
• TF1 placeholder/feed workflows with placeholders_from_data_dicts, placeholders_from_networkxs, get_feed_dict, and make_runnable_in_session.
• Eager/TF2 conversion and tf.function signatures with nest_to_numpy and specs_from_graphs_tuple.
• Tensor GraphsTuple transforms: concat, repeat, identity, stop_gradient, get_graph, and get_num_graphs.
• Completing featureless graphs with fully connected edges or zero-valued node, edge, and global feature tensors.
• Fixed-size batching with GraphsTupleSize, get_graphs_tuple_size, pad_graphs_tuple, remove_graphs_tuple_padding, and get_mask.

Route elsewhere

• For pure NumPy/NetworkX conversion or utils_np round trips, use graph-data.
• For Sonnet modules, Graph Nets blocks, learned message-passing constructors, and model factories, use graph-models.

Version decision

Choose the TensorFlow execution style before writing code:

• TF1 / Sonnet 1 stack: use tf.Session, top-level tf.placeholder, placeholders_from_*, get_feed_dict, and make_runnable_in_session when a graph has None fields.
• TF2 / Sonnet 2 stack: top-level tf.Session and tf.placeholder are not available. Prefer eager tensors, utils_tf.nest_to_numpy, and utils_tf.specs_from_graphs_tuple for tf.function input signatures. Do not route TF2 users to placeholder APIs unless they explicitly own a compatible TF1-style runtime.

Operating procedure

1. Normalize graph data into a GraphsTuple with tensor-valued fields. Keep NumPy/NetworkX-only conversion details in the graph-data sub-skill.
2. Ensure every selected utility's preconditions are true: matching non-None fields for concat, scalar integer graph indices for get_graph, complete fields for specs_from_graphs_tuple, and enough padding capacity for a dummy graph when padding.
3. Add missing zero-valued feature tensors before model or tf.function use when a utility rejects None feature fields.
4. In TF1, convert None fields with make_runnable_in_session immediately before sess.run; in TF2, leave None fields as ordinary Python None unless a utility explicitly requires tensors.
5. Use the bundled smoke script for a quick environment check: python scripts/tf_ops_smoke.py --help, then run it in the intended Graph Nets TensorFlow environment.

References

• API reference
• Workflows
• Troubleshooting