just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are

  // Create a session for this graph.
  CSession csession(graph, s, use_XLA);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  if (!device.empty()) {
    LOG(INFO) << "Setting op Min on device " << device;
  }
  TF_Operation* min = MinWithDevice(feed, one, graph, device, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Run the graph.

If you experience any snags when using TF 2.0, please let us know at the
[TF 2.0 Testing User Group](https://groups.google.com/a/tensorflow.org/forum/?utm_medium=email&utm_source=footer#!forum/testing).
We have a support mailing list as well as weekly testing meetings, and would
love to hear your migration feedback and questions.

## Bug Fixes and Other Changes

*   `tf.contrib`:

// Operations the tape needs to perform on tensors to do backpropagation. Named
// "vspace" because a subset of these are related to a vector space, such as
// adding gradients, getting zeroes, etc. Currently cannot be implemented
// without using tensorflow python code, hence left unspecified here.
//
// Gradient is the type returned by gradient functions. In Python TF it's either

  args = parser.parse_args()

  _TF_WORKSPACE_ROOT = args.workspace
  _TF_BAZELRC = os.path.join(_TF_WORKSPACE_ROOT, _TF_BAZELRC_FILENAME)

  # Make a copy of os.environ to be clear when functions and getting and setting
  # environment variables.
  environ_cp = dict(os.environ)

  try:
    current_bazel_version = retrieve_bazel_version()
  except subprocess.CalledProcessError as e:

      override {
    return func_record_.GetNewRef();
  }

  // For LLVM style RTTI.
  static bool classof(const AbstractFunction* ptr) {
    return ptr->getKind() == kGraph;
  }

 private:
  core::RefCountPtr<FunctionRecord> func_record_;
};
}  // namespace graph
}  // namespace tracing
}  // namespace tensorflow

  RecordMutation(graph, *op, "setting fulltype");
}

void TF_SetRequestedDevice(TF_Graph* graph, TF_Operation* op,
                           const char* device) {
  using tensorflow::RecordMutation;
  mutex_lock l(graph->mu);
  op->node.set_requested_device(device);
  RecordMutation(graph, *op, "setting device");
}

# Non-configurable setting to indicate open source build.
bool_setting(
    name = "oss_setting",
    build_setting_default = if_oss(True, False),
    visibility = ["//visibility:private"],
)

# Setting to use when loading kernels dynamically
config_setting(
    name = "dynamic_loaded_kernels",
    define_values = {
        "dynamic_loaded_kernels": "true",
        "framework_shared_object": "true",
    },

      return FullTypeDef();
    } else {
      return *ft;
    }
  }

  TF_Output output_;

  // For LLVM style RTTI.
  static bool classof(const AbstractTensorHandle* ptr) {
    return ptr->getKind() == kGraph;
  }

 private:
  TF_Graph* graph_;  // For shape inference.
};

// GraphOperation wraps and populates a TF_OperationDescription.
class GraphOperation : public TracingOperation {
 public:

 protected:
  enum AbstractFunctionKind { kGraph, kMlir };
  explicit AbstractFunction(AbstractFunctionKind kind) : kind_(kind) {}

 public:
  // Returns which subclass is this instance of.
  AbstractFunctionKind getKind() const { return kind_; }

  // Returns the AbstractFunction as a FunctionDef.
  virtual Status GetFunctionDef(const FunctionDef**) = 0;

  // Returns a shared reference to the wrapped function.