if [[ "$TFCI_DOCKER_REBUILD_UPLOAD_ENABLE" == 1 ]]; then
    docker push "$TFCI_DOCKER_IMAGE"
  fi
fi

# Keep the existing "tf" container if it's already present.
# The container is not cleaned up automatically! Remove it with:
# docker rm tf
if ! docker container inspect tf >/dev/null 2>&1 ; then
  # Pass all existing TFCI_ variables into the Docker container
  env_file=$(mktemp)
  env | grep ^TFCI_ > "$env_file"

        # to tag names on gcr.io/tensorflow-sigs/build.
        # TF 2.9
        map sigbuild-r2.9 2.9-python3.9
        map sigbuild-r2.9-python3.8 2.9-python3.8
        map sigbuild-r2.9-python3.9 2.9-python3.9
        map sigbuild-r2.9-python3.10 2.9-python3.10
        # TF 2.10
        map sigbuild-r2.10 2.10-python3.9
        map sigbuild-r2.10-python3.8 2.10-python3.8

    *   Added a new boolean argument `allow_fast_lookup` to `tf.nn.embedding_lookup_sparse` and `tf.nn.safe_embedding_lookup_sparse`, which enables a simplified and typically faster lookup procedure.

*   `tf.data`

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

  "$python" -m pip install tf-nightly-cpu
  if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then
    "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
    "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
  fi
fi
# VERY basic check to ensure the [and-cuda] package variant is installable.

      * For TF-GPU - See point 1
      * For TF-CPU - See point 2
   -----------------------------------------------------------------------------------------------
   
   **1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
   
   
   Make sure you are using compatible TF and CUDA versions.
   Please refer following TF version and CUDA version compatibility table.
   

#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     dbg:              Build with debug info
#
# TF version options;
#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA
#     tpu:          Build TF with TPU support
#     cuda:         Build with CUDA support.

		}

		for j, tf := range v.tests {
			switch tf := tf.(type) {
			case testWrite:
				got, err := fw.Write([]byte(tf.str))
				if got != tf.wantCnt || err != tf.wantErr {
					t.Errorf("test %d.%d, Write(%s):\ngot  (%d, %v)\nwant (%d, %v)", i, j, tf.str, got, err, tf.wantCnt, tf.wantErr)
				}
			case testReadFrom:
				f := &testFile{ops: tf.ops}
				got, err := fw.ReadFrom(f)

typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.4.0, < 0.5.0
# 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 ~= 2.18.0.a
# Test dependencies