* Removed the `tff.framework.set_default_executor` API.
* Updated the callers to use the higher-level convienent API `tff.backends.*` instead.

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This change adds a compiler function to the `ExecutionContext` object. Conceptually a `Context` can be thought of as an "environment" which owns compilation and owns execution for a given computation. Additionally, this change replaces `set_default_executor` with higher level functions in order to simplify how contexts are constructed.

* Added compiler function to the `ExecutionContext`.
* Deprecated `set_default_executor`.
* Removed all usage of `set_default_executor` internally.
* Added convenience high level functions that set an execution context:
  * tff.backends.native.set_local_execution_context
* Updated `set_default_executor` call-sites to either use the convenience high level functions or to manually construct a context and use `set_default_context`.

Note that we should consider creating the following convenience high level functions:
  * tff.backends.native.set_remote_execution_context
  * tff.backends.native.set_sizing_execution_context
  * tff.backends.iree.set_iree_execution_context

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Semantic changes:
-  Use the LR schedule with round 0 during the server intializiation.
   Previously this relied on the optimizer builder specifying a default for the
   learning rate, which was overriden in later rounds. Explicitly call with a
   "round 0" learning rate to ensure the documented type is adhered to. This
   can be seen in the type annotations for the `server_optimizer_fn` argument
   to `build_server_init_fn` and `build_fed_avg_process`.

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Taking string rep of flattened ndarray can cause non-equal ndarrays to hash to identical values.

E.g., added test here failed previously--arrays that have identical values on the ends of their flattened reps, but differ in the middle, will result in the same key.

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There are a few users to consider:

1. A developer on TFF wants source at HEAD.
2. A researcher using TFF wants pip package at release in 90% of scenarios and sometimes (ideally rarely) wants nightly, but never wants to build a pip package.
3. A developer making a framework using TFF wants pip release and nightly, but never wants to build a pip package.
4. A researcher upstreaming change to TFF basically has moved from #2 to #3.

Fixes: #878
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Explicitly raise a (clearer) error message when models end up in invalid states due to interleaving graph and eager.

In rare cases code may have run w/o crashing when in these invalid states, but it's safer to error with an explanation rather than risk silent failures/fragile behavior.

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TrainableModel has been removed from TFF for a while.

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  * Fixes `building_blocks.Block` traversal in `transform_preorder`.
  * Extends `unwrap_placement` to handle no-arg lambdas.

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In response to #875

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This is part of the current refactoring of the HLO related dialect.
`xla_hlo` will be reintroduced in a new form later.

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The type signature of `work` was <client_data, state> -> <client_updates, client_stats> where client_updates = <simple_agg, secure_agg>. After this CL, the type signature of `work` becomes <client_data, state> -> client_updates.

* `canonical_form.py`: remove `client_outputs` from `work`; update doc.

* `canonical_form_utils.py`: remove c8 from the canonical form, and change c7 to c6 and c6 to c5; remove `_create_next_with_fake_client_output` function.

* Remove `client_outputs` from `test_utils.py`.

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