// `gradient_checker` only works with model that returns only 1 tensor.
  // Although, `ops::SparseSoftmaxCrossEntropyWithLogits` returns 2 tensors, the
  // second tensor isn't needed for computing gradient so we could safely drop
  // it.
  outputs[0] = loss;
  backprop->Unref();
  return absl::OkStatus();
}

Status BiasAddModel(AbstractContext* ctx,
                    absl::Span<AbstractTensorHandle* const> inputs,

  TF_RETURN_IF_ERROR(
      ops::IdentityN(ctx, inputs, absl::MakeSpan(temp_outputs), "IdentityN"));
  // Although, `ops::IdentityN` returns 2 tensors, the first tensor isn't needed
  // for computing gradient so we could safely drop it.
  outputs[0] = temp_outputs[1];
  temp_outputs[0]->Unref();
  return absl::OkStatus();
}

class CppGradients
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:

# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available
# through bazel. This must be done in a different root directory, //bazel_pip/...,

# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available
# through bazel. This must be done in a different root directory, //bazel_pip/...,

*   `tf.scatter_update` now no longer guarantees that lexicographically largest
    index be used for update when duplicate entries exist.
*   `tf.image.random_crop(image, [height, width])` is now `tf.random_crop(image,
    [height, width, depth])`, and `tf.random_crop` works for any rank (not just
    3-D images). The C++ `RandomCrop` op has been replaced with pure Python.
*   Renamed `tf.test.GetTempDir` and `tf.test.IsBuiltWithCuda` to

//
// Even if error information is recorded in *status, this call discards all
// local resources associated with the session.  The session may not be used
// during or after this call (and the session drops its reference to the
// corresponding graph).
TF_CAPI_EXPORT extern void TF_DeleteSession(TF_Session*, TF_Status* status);

// Run the graph associated with the session starting with the supplied inputs