node [fontname = "Sans"];
   edge [fontname = "Sans"];

   "api" [shape="box", label=<<B>api</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]

   "compileOnlyApi" [shape="box", label=<<B>compileOnlyApi</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]

   "implementation" [shape="box", label=<<B>implementation</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]

		})
	}
	type scoreCase struct {
		classResources classResourceMap
		score          int64
	}
	tests := []struct {
		name  string
		shape helper.FunctionShape
		cases []scoreCase
	}{
		{
			name:  "default shape, single class",
			shape: defaultShape,
			cases: []scoreCase{
				{
					classResourceMap{
						classHDD: &StorageResource{
							Requested: 0,
							Capacity:  100,
						},

// CHECK-NEXT:     subgraphs: [ {
// CHECK-NEXT:       tensors: [ {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         buffer: 1,
// CHECK-NEXT:         name: "arg0",
// CHECK-NEXT:         quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:         },
// CHECK-NEXT:         has_rank: true
// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         type: INT8,
// CHECK-NEXT:         buffer: 2,

    value {
      type: DT_BOOL
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
node {
  name: "x"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
}
versions {

    EXPECT_LT(max_error, 1e-4);
  }

  Scope scope_;
};

TEST_F(ManipGradTest, RollGrad) {
  TensorShape shape({5, 4, 3});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Roll(scope_, x, {2, 1}, {0, 1});
  RunTest(x, shape, y, shape);
}

}  // namespace

    %VarHandleOp, %ctl = VarHandleOp device("/CPU:0") name("x") {_output_shapes = [#tf_type.shape<>], allowed_devices = [], container = "a", dtype = i64, shape = #tf_type.shape<>, shared_name = "x"} : () -> (tensor<!tf_type.resource<tensor<i64>>>)

// 1. does not cause an error
// 2. preserves dtype and shape.
TEST_P(SavedVariableLoadingTest, LoadSavedVariableSuccessful) {
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));

  SavedVariable saved_variable;
  saved_variable.set_dtype(dtype);
  shape.AsProto(saved_variable.mutable_shape());

  std::unique_ptr<Variable> var;

// CHECK:        subgraphs: [ {
// CHECK-NEXT:     tensors: [ {
// CHECK-NEXT:       shape: [ 3, 2 ],
// CHECK-NEXT:       buffer: 1,
// CHECK-NEXT:       name: "serving_default_x",
// CHECK-NEXT:       quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 3, 2 ],
// CHECK-NEXT:       buffer: 2,
// CHECK-NEXT:       name: "tfl.pseudo_const",

  virtual tensorflow::Status TensorHandleStatus() const;

  // Returns tensor shape. If tensor has unknown rank, shape remains untouched.
  virtual tensorflow::Status Shape(
      tensorflow::PartialTensorShape* shape) const = 0;

  // Returns tensor (full) type.
  // While there is no immediate plan to deprecate dtype and shape in favor
  // of only using full type type information, this is a future possibility.
  //

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "z" output_index: 1}
  shape {
    dim { size: 1 }
  }
  type: DT_INT32
}
fetch {
  id { node_name: "x_y_sum" }
}
fetch {
  id { node_name: "x_z_sum" }