// resources (mostly TensorArray and Stack, used in while loop gradients in
// graph mode). Calling this on a context tells it to start a step.
TF_CAPI_EXPORT extern void TFE_ContextStartStep(TFE_Context* ctx);

// Ends a step. When there is no active step (that is, every started step has
// been ended) step containers will be cleared. Note: it is not safe to call

        c.text = p.text
      p.getparent().remove(p)
    # Remove duplicate results of the same exact test (e.g. due to retry
    # attempts)
    for p in testsuite._elem.xpath(".//error | .//failure"):
      # Sharded tests have target names like this:
      # WindowOpsTest.test_tflite_convert0 (<function hann_window at
      #     0x7fc61728dd40>, 10, False, tf.float32)
      # Where 0x... is a thread ID (or something) that is not important for

        // (where the value starts on the host), without allowing other implicit
        // copies/broadcasts. Other implicit copies may be supported eventually,
        // but need special handling for gradients (gradient of copy-on is not
        // just copy-off but includes a sum) and consideration of performance.
        //
        // TODO(allenl): There may be smarter ways to do this copy in some

automatically on GitHub.

If you want to contribute, start working through the TensorFlow codebase,
navigate to the
[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.

    absl::MutexLock l(&gcs_file->buffer_mutex);
    size_t buffer_end = gcs_file->buffer_start + gcs_file->buffer.size();
    size_t copy_size = 0;
    if (offset < buffer_end && gcs_file->buffer_start) {
      copy_size = (std::min)(n, static_cast<size_t>(buffer_end - offset));
      memcpy(buffer,
             gcs_file->buffer.data() + (offset - gcs_file->buffer_start),
             copy_size);
    }
    bool consumed_buffer_to_eof =

  /// structure in Section 4).
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if transaction successfuly started.
  ///   * Must set `status` to `TF_FAILED_PRECONDITION` if multiple transactions
  ///     are not supported
  ///   * Might use any other error value for `status` to signal other errors.
  int (*start_transaction)(const TF_Filesystem* filesystem,
                           TF_TransactionToken** token, TF_Status* status);

  // Note that future collectives with the same context do not work at the
  // moment; once canceled, the collective executor requires the program to be
  // restarted / context to be reset.
}

TEST(PARALLEL_DEVICE_LIB, TestDifferentShapes) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

    states. Specifically, it no longer ever drops the `c` (memory) state of an
    `LSTMStateTuple`. The new behavior leads to proper dropout behavior for
    LSTMs and stacked LSTMs. This bug fix follows recommendations from published
    literature, but is a behavioral change. State dropout behavior may be
    customized via the new `dropout_state_filter_visitor` argument.

      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of

executed.

The security impact of skipping those checks should be low, since the attack
scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the
recommendation is not to serve models using Eager mode since it also has
performance limitations.

## Multi-Tenant environments