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requirements_lock_3_12.txt
# h5py # jax # keras-nightly # ml-dtypes # opt-einsum # scipy # tb-nightly opt-einsum==3.3.0 \ --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \ --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549 # via # -r requirements.in # jax packaging==23.2 \
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requirements_lock_3_9.txt
# h5py # jax # keras-nightly # ml-dtypes # opt-einsum # scipy # tb-nightly opt-einsum==3.3.0 \ --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \ --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549 # via # -r requirements.in # jax packaging==23.2 \
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requirements_lock_3_10.txt
# h5py # jax # keras-nightly # ml-dtypes # opt-einsum # scipy # tb-nightly opt-einsum==3.3.0 \ --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \ --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549 # via # -r requirements.in # jax packaging==23.2 \
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ci/official/requirements_updater/requirements.in
keras-nightly ~= 3.0.0.dev tb-nightly ~= 2.17.0.a # Test dependencies grpcio >= 1.24.3, < 2.0 portpicker == 1.6.0 scipy == 1.11.3 requests == 2.31.0 packaging==23.2 setuptools==68.2.2
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.github/workflows/osv-scanner-scheduled.yml
--lockfile=requirements.txt:./requirements_lock_3_12.txt --lockfile=requirements.txt:./ci/official/containers/linux_arm64/devel.requirements.txt --lockfile=requirements.txt:./ci/official/containers/linux_arm64/jax.requirements.txt
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requirements_lock_3_11.txt
# h5py # jax # keras-nightly # ml-dtypes # opt-einsum # scipy # tb-nightly opt-einsum==3.3.0 \ --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \ --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549 # via # -r requirements.in # jax packaging==23.2 \
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tensorflow/BUILD
"//tensorflow/compiler/mlir/lite/python:flatbuffer_to_mlir", "//tensorflow/compiler/mlir/lite/python:graphdef_to_tfl_flatbuffer", "//tensorflow/compiler/mlir/lite/python:jax_to_tfl_flatbuffer", "//tensorflow/compiler/mlir/lite/python:saved_model_to_tfl_flatbuffer", "//tensorflow/compiler/mlir/lite/quantization/lite:quantize_model",
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RELEASE.md
tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32) result = client.multiply(a, b)` * `tf.lite`: * Add experimental API `experimental_from_jax` to support conversion from Jax models to TensorFlow Lite. * Support uint32 data type for cast op. * Support int8 data type for cast op. * Add experimental quantization debugger `tf.lite.QuantizationDebugger`
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