TF_Tensor* BoolTensor(int32_t v);

// Create a tensor with values of type TF_INT8 provided by `values`.
TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values);

// Create a tensor with values of type TF_INT32 provided by `values`.
TF_Tensor* Int32Tensor(const int64_t* dims, int num_dims,
                       const int32_t* values);

// Create 1 dimensional tensor with values from `values`

/// in addition with the data discussed in this section. Each plugin author can
/// use a custom scheme, but it should only relate to changes in plugin code.
/// This section only touches metadata related to the versioning of this
/// interface that is shared by all possible plugins.
///
/// The API number increases whenever we break API compatibility while still

void TestRemoteExecuteSilentCopiesOp(bool async, bool remote,
                                     bool remote_func_outputs = false) {
  return TestRemoteExecuteSilentCopies(async, remote, /*func=*/false,
                                       /*heavy_load_on_streaming_rpc=*/false,
                                       remote_func_outputs);
}

TEST(CAPI, RemoteExecuteSilentCopies) {

# under the root of srcjars jars created by bazel, rather than under
# the maven-style src/main/java subdirectory.
#
# Bazel manages annotation generated source as follows: First, it
# calls javac with options that create generated files under a
# bazel-out directory. Next, it archives the generated source files
# into a srcjar directly under the root. There doesn't appear to be a
# simple way to parameterize this from bazel, hence this helper to

  void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
            std::initializer_list<TF_Output> outputs,
            const std::vector<std::vector<int32_t>>& expected_results) {
    // Create a session for this graph
    CSession csession(host_graph_, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    // Run
    csession.SetInputs(inputs);
    csession.SetOutputs(outputs);

        tf.TensorSpec([], dtypes.int32) ]) def _remote_multiply(a, b): return
        tf.math.multiply(a, b)

        server.register("multiply", _remote_multiply) ```

    *   Example usage to create client: `python client =
        tf.distribute.experimental.rpc.Client.create("grpc", address) a =
        tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32)

TFE_MonitoringSampler0* TFE_MonitoringNewSampler0(
    const char* name, TFE_MonitoringBuckets* buckets, TF_Status* status,
    const char* description) {
  auto* result = new TFE_MonitoringSampler0(
      {name, buckets->create_buckets(), description});
  tsl::Set_TF_Status_from_Status(status, result->sampler->GetStatus());
  if (!result->sampler->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;
}

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack

  return th;
}

TFE_TensorHandle* TestVariable(TFE_Context* ctx, float value,
                               const tensorflow::string& device_name) {
  TF_Status* status = TF_NewStatus();
  // Create the variable handle.
  TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);

     *    dA =  U
     *    dB = -U
     *
     */

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

    // Grad for B
    // negate the upstream grad
    std::string name = "Neg_Sub_Grad_B";
    TF_RETURN_IF_ERROR(
        ops::Neg(ctx, grad_outputs[0], &grad_inputs[1], name.c_str()));

    return absl::OkStatus();
  }