metadata.type = TF_ATTR_INT;
          } else if (typestr == "list(float)") {
            metadata.type = TF_ATTR_FLOAT;
          } else if (typestr == "list(bool)") {
            metadata.type = TF_ATTR_BOOL;
          } else if (typestr == "list(type)") {
            metadata.type = TF_ATTR_TYPE;
          } else if (typestr == "list(shape)") {
            metadata.type = TF_ATTR_SHAPE;

  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);
  TFE_OpSetAttrString(op, "container", "localhost", 0);
  TFE_OpSetAttrString(op, "shared_name", "", 0);
  if (!device_name.empty()) {

  tensorflow::TensorShape shape(variable.shape());
  tensorflow::DataType dtype = variable.dtype();
  std::vector<std::string> component_devices;

  for (const auto& component :
       variable.experimental_distributed_variable_components()) {
    component_devices.push_back(component.device());
  }

  TF_RETURN_IF_ERROR(Variable::CreateUninitialized(
      ctx, dtype, shape, name,

      auto* out_shape_and_type = handle_data.add_shape_and_type();
      ic->ShapeHandleToProto(p.shape, out_shape_and_type->mutable_shape());
      out_shape_and_type->set_dtype(p.dtype);
      *out_shape_and_type->mutable_type() = p.type;
    }
  }
  string result;
  handle_data.SerializeToString(&result);
  return result;
}

void SetHandleShapeAndType(TF_Graph* graph, TF_Output output, const void* proto,

REGISTER_NO_GRADIENT_OP("Floor");

// Conjugate helper function returns the conjugate of an Output if it
// is complex valued.
Output ConjugateHelper(const Scope& scope, const Output& out) {
  DataType dtype = out.type();
  if (dtype == DT_COMPLEX64 || dtype == DT_COMPLEX128) {
    return Conj(scope, out);
  } else {
    return out;
  }
}

// TODO(andydavis) Add control dependencies to gradient functions (as needed).

      tensorflow::OpInfo::TensorProperties in_props;
      Status s = tensorflow::BufferToMessage(in_props_buf[0], &in_props);
      TF_ASSERT_OK(s);

      EXPECT_EQ(DT_FLOAT, in_props.dtype());
      EXPECT_FALSE(in_props.shape().unknown_rank());
      EXPECT_EQ(2, in_props.shape().dim_size());
      EXPECT_EQ(10, in_props.shape().dim(0).size());
      EXPECT_EQ(1, in_props.shape().dim(1).size());

  }
  strings::StrAppend(&ret, "}");
  return ret;
}

string PrintTensor(const TensorProto& tensor_proto) {
  Tensor t(tensor_proto.dtype());
  CHECK(t.FromProto(tensor_proto));
  const int64_t num_elts = t.NumElements();
  switch (t.dtype()) {
    case DT_FLOAT:
      return PrintArray(num_elts, t.flat<float>().data());
    case DT_DOUBLE:
      return PrintArray(num_elts, t.flat<double>().data());

  }

  // Derive the output types. The result type is derived by using the
  // attributes attched to the result type of the signature. The attribute
  // value should be either in the attribute argument list or the derived
  // attribute from the input tensors. All the result type
  // are unranked, and shape inference should be applied afterwards.
  SmallVector<Type, 4> output_types;

  } else {
    return se::DeviceMemoryBase(const_cast<char*>(tensor.tensor_data().data()),
                                tensor.tensor_data().size());
  }
}

Status XlaTensor::AllocateShapedBuffer(DataType dtype,
                                       const xla::Shape& on_device_shape,
                                       xla::LocalClient* client,
                                       int device_ordinal) {
  xla::Shape on_host_shape =

  v->set_tag(tag.data(), tag.size());
  histo.EncodeToProto(v->mutable_histo(), false /* Drop zero buckets */);

  Safe_TF_TensorPtr summary_tensor(TF_AllocateOutput(
      /*context=*/ctx, /*index=*/0, /*dtype=*/TF_ExpectedOutputDataType(ctx, 0),
      /*dims=*/nullptr, /*num_dims=*/0,
      /*len=*/sizeof(tensorflow::tstring), status.get()));

  if (TF_GetCode(status.get()) != TF_OK) {