// encounters an error.
  absl::MutexLock l(&block->mu);
  TF_SetStatus(status, TF_OK, "");
  while (true) {
    switch (block->state) {
      case FetchState::ERROR:
        // TF_FALLTHROUGH_INTENDED
      case FetchState::CREATED:
        block->state = FetchState::FETCHING;
        block->mu.Unlock();  // Release the lock while making the API call.
        block->data.clear();

// Fetches the current number of inputs attached to `op`.
//
// Does not use the operation's definition to determine how many inputs should
// be attached. It is intended for use with TFE_OpGetFlatInput to inspect an
// already-finalized operation.
//
// Note that TFE_OpGetFlatInputCount and TFE_OpGetFlatInput operate on a flat

```

To specify the version via a Bazel command argument, use the following:

```
--repo_env=TF_PYTHON_VERSION=3.11
```

## Requirements updater

Requirements updater is a standalone tool, intended to simplify process of
updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed
requirements file for each version, with hashes specified for each

// Implementation detail for the unified execution APIs for Eager and tracing
// backends (graph/MLIR).
//
// This defines a set of abstract classes that are intended to provide the
// functionality of the opaque C types exposed in the public APIs defined in the
// `c_api_unified_experimental.h` header.
// =============================================================================

read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for
internal communication only. It is not built for use in untrusted environments
or networks.

For performance reasons, the default TensorFlow server does not include any

                .data());
      }
    } break;
    case tensorflow::AttrValue::kTensor:
      TF_FALLTHROUGH_INTENDED;
    case tensorflow::AttrValue::kPlaceholder:
      TF_FALLTHROUGH_INTENDED;
    case tensorflow::AttrValue::VALUE_NOT_SET:
      TF_SetStatus(
          status, TF_UNIMPLEMENTED,

        ":fuchsia",
        ":emscripten",
        ":ios",
    ],
    visibility = ["//visibility:public"],
)

# This flag disables all google production dependencies, intended for
# applications run with non-prod environment.
# TODO(timshen): Currently this option only disables some dependencies.
# See b/122528503.
# copybara:uncomment_begin(google-only)
# bool_flag(

    *   This version removes the `tensorrt` Python package dependencies from the
        tensorflow[and-cuda] installation method to ensure `pip install
        tensorflow[and-cuda]` works as originally intended for TensorFlow 2.15.
    *   Support for TensorRT is otherwise unaffected as long as TensorRT is
        already installed on the system.
*   Using .post1 instead of a full minor release allowed us to push this release

// Aborts all ongoing collectives with the specified status. After abortion,
// subsequent collectives will error with this status immediately. To reset the
// collectives, create a new EagerContext.
//
// This is intended to be used when a peer failure is detected.
TF_CAPI_EXPORT extern void TFE_AbortCollectiveOps(TFE_Context* ctx,
                                                  TF_Status* status);

# PYTHON TESTS run a suite of Python tests intended for verifying that the Python wheel
# will work properly. These are usually run Nightly or upon Release.
# CPU WHEEL