TFCI_ARTIFACT_FINAL_PYPI_ARGS="--config-file=$KOKORO_KEYSTORE_DIR/73361_tensorflow_pypirc_using_global_api_token --repository pypi-warehouse"
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=1
TFCI_ARTIFACT_LATEST_GCS_URI="gs://tensorflow/versions/latest/"
TFCI_ARTIFACT_STAGING_GCS_ENABLE=1
TFCI_ARTIFACT_STAGING_GCS_URI="gs://tensorflow-ci-artifact-staging/staging/versions/${_LOUHI_EXECUTION_ID:-$(git rev-parse HEAD)}/"

wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
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

TFCI_ARTIFACT_FINAL_PYPI_ARGS="--config-file=$KOKORO_KEYSTORE_DIR/73361_tensorflow_pypirc_using_global_api_token --repository pypi-warehouse"
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=1
TFCI_ARTIFACT_LATEST_GCS_URI="gs://tensorflow/nightly/latest/"
TFCI_ARTIFACT_STAGING_GCS_ENABLE=1
TFCI_ARTIFACT_STAGING_GCS_URI="gs://tensorflow-ci-artifact-staging/staging/nightly/$(git rev-parse HEAD)/"

# 2. The internal version numbers get changed

# Disable ALL uploads of any kind.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=
TFCI_ARTIFACT_FINAL_GCS_SA_PATH=
TFCI_ARTIFACT_FINAL_GCS_URI=
TFCI_ARTIFACT_FINAL_PYPI_ARGS=
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=
TFCI_ARTIFACT_LATEST_GCS_URI=
TFCI_ARTIFACT_STAGING_GCS_ENABLE=
TFCI_ARTIFACT_STAGING_GCS_URI=

TFCI_ARTIFACT_FINAL_GCS_ENABLE=
TFCI_ARTIFACT_FINAL_GCS_SA_PATH=
TFCI_ARTIFACT_FINAL_GCS_URI=
TFCI_ARTIFACT_FINAL_PYPI_ARGS=
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=
TFCI_ARTIFACT_LATEST_GCS_URI=
TFCI_ARTIFACT_STAGING_GCS_ENABLE=
TFCI_ARTIFACT_STAGING_GCS_URI=
TFCI_BAZEL_BAZELRC_ARGS=
TFCI_BAZEL_COMMON_ARGS=
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=
TFCI_BUILD_PIP_PACKAGE_ARGS=
TFCI_DOCKER_ARGS=

    index be used for update when duplicate entries exist.
*   `tf.image.random_crop(image, [height, width])` is now `tf.random_crop(image,
    [height, width, depth])`, and `tf.random_crop` works for any rank (not just
    3-D images). The C++ `RandomCrop` op has been replaced with pure Python.
*   Renamed `tf.test.GetTempDir` and `tf.test.IsBuiltWithCuda` to