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Created with Raphaël 2.2.019Mar181716151110985432128Feb2726252423222119181718171613121110965432129Jan2827262522211915141312118765430Dec292824232221171615141198753130Nov2726252423201918171612111096543230Oct292827232221201917161514131211987652129Sep2825242322211916151413121110986432128Aug272625212019181715141312Merge main -> googleInternal infrastructure change.cl/363925910 up…cl/363925910 upstream/cl/363925910InternalRelax the type annotations on `type_to_tensor_structure`Adds support in the EagerTFExecutor for embedding unnamed struct value with named type spec.Adds learning/optimizers/ directory and initial optimizer abstractions.[no-op] Update `BUILD` files to use the `iree` python library.Set `stackoverflow_test` test `size` to `large`, the `load_word_counts` API is potentially multi-machine.Adds integration test coverage for worker going down with fix default number of clients per round.Adds more actionable message to mismatch between static num_clients and requested cardinalities.Testing presubmitscl/363245594cl/363245594Merge main -> googleInternal testing.cl/363210288 up…cl/363210288 upstream/cl/363210288Merge pull request #1235 from legenderyLuke:patch-1Update TFF package documentation.Export a handful of TFF type utilities under tff.types.*Increases num_iterations in dp_factory_test test_noise to reduce flake rate.Remove fixed bug links from documentation.Add a training loop to TensorFlow Federated for running iterative process with checkpoints, metrics managers, and validation functions.Remove `impl` package visibility exception.Removes rpc_mode argument from TFF remove executor examples and tutorials.Restrict visibility rules in `tools` package.Update gcp_setup.mdAlign the checkpoint and metrics manager API.Uses LRU cache for executor factory internal caches.Replaces remove_called_lambdas_and_blocks with transform_to_local_call_dominant.Add type_to_tensor_structureCopy ClientData class and subclasses to tff.simulation.datasets.Add broadcast_process argument to build_federated_evaluation to allow passing in a MeasuredProcess that can encode/decode during eval tasks similar to what is done for train tasks. The broadcast_process must not require state; for example, a uniform_quantization encoder can be used to compress the model that is being sent from server to clients for evaluation.InternalAdd optional rounds_per_checkpoint argument to enable periodic checkpoint saving without the need to hardcode round number checks into a training loop.Create a LearningProcess template specializing IterativeProcess.Add `.bazelversion` configuration.Adds possibility of unweighted aggregation to tff.learning.secure_aggregator.Adds possibility of unweighted aggregation to tff.learning.compression_aggregator.Skips the first notebook cell when testing.Adds possibility of unweighted aggregation to tff.learning.robust_aggregator.Expose `UnweightedReservoirSamplingFactory` in the `tff.aggregators` package.Expose `SqlClientData` in the pip package public API.Remove selection-by-name support from executors