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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
//tensorflow/python:compare_test_proto_py //tensorflow/python/framework:test_ops_2 //tensorflow/python/framework:test_file_system.so //tensorflow/python/debug:grpc_tensorflow_server.par //tensorflow/python/feature_column:vocabulary_testdata //tensorflow/python/util:nest_test_main_lib //tensorflow/lite/experimental/examples/lstm:rnn_cell //tensorflow/lite/experimental/examples/lstm:rnn_cell.py
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ci/official/utilities/code_check_full.bats
//tensorflow/python:tf_optimizer //tensorflow/python:compare_test_proto_py //tensorflow/python/framework:test_file_system.so //tensorflow/python/debug:grpc_tensorflow_server.par //tensorflow/python/feature_column:vocabulary_testdata //tensorflow/python/util:nest_test_main_lib //tensorflow/lite/experimental/examples/lstm:rnn_cell //tensorflow/lite/experimental/examples/lstm:rnn_cell.py
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RELEASE.md
vocabulary and embeddings during the training process. This layer maintains a hash table to track the most up-to-date vocabulary based on the inputs received by the layer and the eviction policy. When this layer is used with an `UpdateEmbeddingCallback`, which is a time-based callback, the vocabulary lookup tensor is updated at the time interval set in the
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