A problem shows up with using subclasses of `tff.learning.Model`: after wrapping the model as an EnhancedModel, one cannot access the methods that are specifically defined by the subclass model.

This CL removes the EnhancedModel wrapper used when computing baseline metrics and training personalized models. This makes sure that users can access the full functionality of the model returned by `model_fn`.

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* 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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