TestScalarTensorHandle<T, datatype>(eager_ctx, value);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Places data from `t` into *result_tensor.
Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor);
}  // namespace tensorflow

  TF_Graph* g1 = TF_NewGraph();
  TF_Graph* g2 = TF_NewGraph();

  TF_Operation* feed = Placeholder(g1, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Attempt to create node in g2 with input from g1
  Neg(feed, g2, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s));
  EXPECT_STREQ("foo", TF_Message(s));

  TF_DeleteGraph(g1);
  TF_DeleteGraph(g2);
  TF_DeleteStatus(s);
}
*/

from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow  # pylint: disable=unused-import
from tensorflow.python.tools import module_util as _module_util
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2

                                                  TF_Status* status,
                                                  const char* description) {
  auto* result = new TFE_MonitoringCounter0({name, description});
  tsl::Set_TF_Status_from_Status(status, result->counter->GetStatus());
  if (!result->counter->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;
}

    *   Upgrade Flatbuffers v2.0.5 from v1.12.0

*   `tf.keras`:

    *   `EinsumDense` layer is moved from experimental to core. Its import path
        is moved from `tf.keras.layers.experimental.EinsumDense` to
        `tf.keras.layers.EinsumDense`.
    *   Added `tf.keras.utils.audio_dataset_from_directory` utility to easily
        generate audio classification datasets from directories of `.wav` files.

    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

2.  If you are changing the code and the docstring of a class/function/method,
    then you will need to
    [build TensorFlow from source](https://www.tensorflow.org/install/source).
    Once you are setup to build from source, you can run the tests:

    ```bash

      ->set_disable_optimize_for_static_graph(true);
}

void TF_TensorFromProto(const TF_Buffer* from, TF_Tensor* to,
                        TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");
  tensorflow::TensorProto from_tensor_proto;
  status->status = BufferToMessage(from, &from_tensor_proto);
  if (!status->status.ok()) {
    return;
  }
  status->status =

     *
     *    dA = U
     *    dbias = reduceSum(U, dims = channel_dim)
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    DCHECK(upstream_grad);

    // Recover data format from forward pass for gradient.
    std::string data_format;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("data_format", &data_format));

    // Grad for A
    grad_inputs[0] = upstream_grad;
    grad_inputs[0]->Ref();

# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

load("@//tensorflow:workspace2.bzl", "tf_workspace2")

tf_workspace2()

# shared object directly.
#
# For example, from Python tf.load_op_library loads a custom op library (via
# dlopen() on Linux), the library finds libtensorflow_framework.so (no
# filesystem search takes place, since libtensorflow_framework.so has already
# been loaded by pywrap_tensorflow) and registers its ops and kernels via
# REGISTER_OP and REGISTER_KERNEL_BUILDER (which use symbols from