1.  `envs/*` are lists of variables made with bash syntax. A user must set a
    `TFCI` env param pointing to a list of `env` files.
2.  `utilities/setup.sh`, initialized by all top-level scripts, reads and sets
    values from those `TFCI` paths.
    -   `set -a` / `set -o allexport` exports the variables from `env` files so
        all scripts can use them.
    -   `utilities/setup_docker.sh` creates a container called `tf` with all

"""
contrib = _LazyLoader("contrib", globals(), "tensorflow.contrib",
                      _CONTRIB_WARNING)
# The templated code that replaces the placeholder above sometimes
# sets the __all__ variable. If it does, we have to be sure to add
# "contrib".
if "__all__" in vars():
  vars()["__all__"].append("contrib")

from tensorflow.python.platform import flags

    TFE_MonitoringSampler2* sampler);
TF_CAPI_EXPORT extern TFE_MonitoringSamplerCell* TFE_MonitoringGetCellSampler2(
    TFE_MonitoringSampler2* sampler, const char* label1, const char* label2);

// Sets whether to use TFRT
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetTfrt(TFE_ContextOptions*,
                                                     bool use_tfrt);

    constraint_values = if_google(
        ["//third_party/bazel_platforms/os:fuchsia"],
        [],
    ),
    values = if_oss(
        # TODO(b/149248802) When we have a Fuchsia Bazel SDK update to use the values it sets.
        {"cpu": "fuchsia"},
        {},
    ),
    visibility = ["//visibility:public"],
)

config_setting(
    name = "fuchsia_x86_64",
    constraint_values = if_google(

  //
  // Executes the backward function in order to trace its gradient, which will
  // waste computation if executing eagerly (when graph building the unneeded
  // computation is pruned). Temporarily sets `backward_tape` so that
  // Accumulate will forward op executions to the tape while the backward
  // function is running; this effectively adds the backward tape to the active

  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

  explicit TfDlManagedTensorCtx(const TensorReference& ref) : reference(ref) {}
};

// Gets tensor from eager tensor handle.
const Tensor* GetTensorFromHandle(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;

    change.
-   You make the change and submit it for the review again.
-   This cycle repeats itself until the PR gets approved.
-   Note: As a friendly reminder, we may reach out to you if the PR is awaiting
    your response for more than 2 weeks.

**4. Approved**

-   Once the PR is approved, it gets `kokoro:force-run` label applied and it
    initiates CI/CD tests.
-   We can't move forward if these tests fail.

*   `tf.experimental.dtensor`

    *   Deprecated `dtensor.run_on` in favor of `dtensor.default_mesh` to correctly indicate that the context does not override the mesh that the ops and functions will run on, it only sets a fallback default mesh.

                                               TF_Status* status);

// Gets the `PJRT_Client*` stored in TF global ResourceManager.
TF_CAPI_EXPORT extern PJRT_Client* TF_GetPjRtCClient(const char* device_type,
                                                     TF_Status* status);

// Gets the `PJRT_Buffer*` stored in the tensor. The status will contain error
// if the tensor does not have a `PjRtCApiBuffer`.

build:android_x86_64 --fat_apk_cpu=x86_64

# Build everything statically for Android since all static libs are later
# bundled together into a single .so for deployment.
build:android --dynamic_mode=off

# Sets the default Apple platform to macOS.
build:macos --apple_platform_type=macos

# gRPC on MacOS requires this #define
build:macos --copt=-DGRPC_BAZEL_BUILD

# Avoid hitting command line argument limit