no_reply += '\n'

  if enabled_by_default:
    question += ' [Y/n]: '
  else:
    question += ' [y/N]: '

  var = environ_cp.get(var_name)
  if var is not None:
    var_content = var.strip().lower()
    true_strings = ('1', 't', 'true', 'y', 'yes')
    false_strings = ('0', 'f', 'false', 'n', 'no')
    if var_content in true_strings:
      var = True
    elif var_content in false_strings:
      var = False

# Where component owners are known, add them here.

/tensorflow/c/eager @qqfish
/tensorflow/core/common_runtime/eager @qqfish
/tenosrflow/core/debug @caisq
/tensorflow/core/kernels/mkl/ @penpornk
/tensorflow/core/kernels/sparse/ @penpornk
/tensorflow/core/nccl/ @azaks2 @chsigg
/tensorflow/python/autograph/ @mdanatg
/tensorflow/python/debug @caisq
/tensorflow/python/eager @rohan100jain
/tensorflow/tools/docs/ @markdaoust

    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

#### General guidelines and philosophy for contribution

*   Include unit tests when you contribute new features, as they help to a)
    prove that your code works correctly, and b) guard against future breaking
    changes to lower the maintenance cost.
*   Bug fixes also generally require unit tests, because the presence of bugs
    usually indicates insufficient test coverage.
*   Keep API compatibility in mind when you change code in core TensorFlow,

// have been executed. Note that "execution" here refers to kernel execution /
// scheduling of copies, etc. Similar to sync execution, it doesn't guarantee
// that lower level device queues (like GPU streams) have been flushed.
//
// This call may not block for execution of ops enqueued concurrently with this
// call.
TF_CAPI_EXPORT extern void TFE_ExecutorWaitForAllPendingNodes(

    }
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,
              const std::vector<TF_Operation*>& inputs,
              const std::vector<TF_Operation*>& outputs,
              const std::vector<string>& output_names,
              bool expect_failure = false) {
    DefineT(num_opers, opers, ToOutput(inputs), ToOutput(outputs), output_names,
            expect_failure);

vulnerabilities to the vendor of the affected hardware accelerator.

## Reporting vulnerabilities

### Vulnerabilities in TensorFlow

This document covers different use cases for TensorFlow together with comments
whether these uses were recommended or considered safe, or where we recommend
some form of isolation when dealing with untrusted data. As a result, this

      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,
                             /*opers=*/nullptr, 1, inputs, 1, outputs,
                             /*output_names=*/nullptr,
                             /*opts=*/nullptr, description, s.get());
  ASSERT_EQ(TF_OK, TF_GetCode(s.get())) << TF_Message(s.get());

*   `tf.data`

    * `tf.data` now has an `autotune_options.initial_parallelism` option to
      control the initial parallelism setting used by autotune before the data
      pipeline has started running. The default is 16. A lower value reduces
      initial memory usage, while a higher value improves startup time.

## Keras

*  `keras.layers.experimental.DynamicEmbedding`
    * Added `DynamicEmbedding` Keras layer