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`tff.type_at_server` and `tff.type_at_clients` are a proposed solution
to the ergonomic issues of `tff.FederatedType(..., tff.CLIENTS)`
and `tff.FederatedType(..., tff.SERVER)`.

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This was requested in order to ensure that TFF computations
return the correct type.

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* Removed duplicate symbols.
* Moved initializing a default execution context to the top level package.
* Removed the circular Python import guards.

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This change moves and renames `AnonymousTuple` and its `Type`-based
brethren. It re-exports the new types under the old names to allow
for a more gradual transition to the new API.

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Added a new `tensorflow_docs` public filter and removed the `_allowed_symbols` variables from the TFF packages.

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This package contains functions and utilities that can make testing usage of TFF
easier or more convenient and parallels the `tf.test` package.

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* Moved class and tests to `templates` package.
* Renamed usage of `computation_utils.IterativeProcess` in TFF to the new module.

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This intrinsic is similar to federated_value, but accepts a no-argument lambda
rather than an unplaced value. This will clarify the behavior of the intrinsic
(eval on all clients separately rather than eval and then broadcast), and
unblocks the effort to ban unplaced values.

PiperOrigin-RevId: 293898477

This change is part one of three:

1. Add `secure_sum` intrinsic to TFF.
2. Update transformations to support the `secure_sum` intrinsic.
3. Update canonical form to support the `secure_sum` intrinsic.

Here is an example using the API:

```python
  @computations.federated_computation
  def init_fn():
    """The `init` function for `computation_utils.IterativeProcess`."""
    return intrinsics.federated_value([0, 0], placements.SERVER)

  @computations.tf_computation(tf.int32, tf.int32], tf.int32]])
  def work(client_data, client_input):
    del client_data  # Unused
    del client_input  # Unused
    return [1, 1]

  @computations.federated_computation([
      computation_types.FederatedType([tf.int32, tf.int32], placements.SERVER),
      computation_types.FederatedType(tf.int32, placements.CLIENTS),
  ])
  def next_fn(server_state, client_data):
    """The `next` function for `computation_utils.IterativeProcess`."""
    client_input = intrinsics.federated_broadcast(server_state)
    c3 = intrinsics.federated_zip([client_data, client_input])
    client_updates = intrinsics.federated_map(work, c3)
    federated_update = intrinsics.federated_sum(client_updates[0])
    secure_update = intrinsics.secure_sum(client_updates[1], 8)
    new_server_state = intrinsics.federated_zip([federated_update, secure_update])
    server_output = intrinsics.federated_value([], placements.SERVER)
    return new_server_state, server_output

  ip = computation_utils.IterativeProcess(init_fn, next_fn)
```

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Fixes #740

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