map sigbuild-r2.12-python3.9 2.12-python3.9
        map sigbuild-r2.12-python3.10 2.12-python3.10
        map sigbuild-r2.12-python3.11 2.12-python3.11
        # TF 2.12 + Clang (containers are the same, but env vars in configs.bzl are different)
        map sigbuild-r2.12-clang 2.12-python3.9
        map sigbuild-r2.12-clang-python3.8 2.12-python3.8
        map sigbuild-r2.12-clang-python3.9 2.12-python3.9

# Helper function to copy a srcjar after moving any source files
# directly under the root to the "maven-style" src/main/java layout
#
# Source files generated by annotation processors appear directly
# under the root of srcjars jars created by bazel, rather than under
# the maven-style src/main/java subdirectory.
#
# Bazel manages annotation generated source as follows: First, it
# calls javac with options that create generated files under a

  environ_cp['TF_CUDA_COMPUTE_CAPABILITIES'] = tf_cuda_compute_capabilities
  write_action_env_to_bazelrc(
      'TF_CUDA_COMPUTE_CAPABILITIES', tf_cuda_compute_capabilities
  )


def set_other_cuda_vars(environ_cp):
  """Set other CUDA related variables."""
  # If CUDA is enabled, always use GPU during build and test.
  if environ_cp.get('TF_CUDA_CLANG') == '1':
    write_to_bazelrc('build --config=cuda_clang')

  if (!ValidateInputWhileParams(*params, status)) return;

  TF_Graph* parent = params->cond_graph->parent;
  TF_Output* parent_inputs = params->cond_graph->parent_inputs;
  int num_loop_vars = params->ninputs;

  mutex_lock l(parent->mu);

  // 'cond_fn' copies the cond graph into the parent graph.
  tensorflow::ops::CondGraphBuilderFn cond_fn =

# Helper function to copy a srcjar after moving any source files
# directly under the root to the "maven-style" src/main/java layout
#
# Source files generated by annotation processors appear directly
# under the root of srcjars jars created by bazel, rather than under
# the maven-style src/main/java subdirectory.
#
# Bazel manages annotation generated source as follows: First, it
# calls javac with options that create generated files under a

        `tf.quantization.fake_quant_with_min_max_args_gradient`,
        `tf.quantization.fake_quant_with_min_max_vars`,
        `tf.quantization.fake_quant_with_min_max_vars_gradient`,
        `tf.quantization.fake_quant_with_min_max_vars_per_channel`,
        `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient`.
    *   New endpoints in tf.strings namespace: `tf.strings.join` (corresponds to

            CACHEBUSTER=${{ steps.vars.outputs.DATE }}
          tags: |
            tensorflow/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
            gcr.io/tensorflow-sigs/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-from: type=registry,ref=tensorflow/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-to: type=inline
      -

}

void TF_LockVariableInfos(TF_VariableInfo** vars, int num_vars,
                          TF_Status* status) {
  std::vector<tensorflow::VariableInfo*> variable_ptrs;
  variable_ptrs.reserve(num_vars);
  for (int i = 0; i < num_vars; ++i) {
    variable_ptrs.push_back(&(vars[i]->var_info));
  }
  absl::Status cc_status = LockVariables(absl::MakeSpan(variable_ptrs));

    source "./ci/official/envs/$env_file"
  done
  set -u
  echo '==TFCI==: Evaluated the following TFCI variables from $TFCI:'
  export -p | grep TFCI

  # Load those stored pre-existing TFCI_ vars, if any
  if [[ -s "$FROM_ENV" ]]; then
    echo '==TFCI==: NOTE: Loading the following env parameters, which were'
    echo 'already set in the shell environment. If you want to disable this'