text: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import copy
  import functools
  import itertools
  import multiprocessing.pool
  import os
  import re
  import sys
  import time
  import weakref
  
  from absl.testing import parameterized
  import numpy
  
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.core.protobuf import rewriter_config_pb2
  from tensorflow.python.autograph.core import ag_ctx
  from tensorflow.python.autograph.lang import directives
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import cancellation
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import function
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import config
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import func_graph
  from tensorflow.python.framework import function as tf_function
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import test_ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.framework import type_spec
  from tensorflow.python.layers import convolutional
  from tensorflow.python.module import module
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import clip_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import data_flow_ops
  from tensorflow.python.ops import functional_ops
  from tensorflow.python.ops import gen_functional_ops
  from tensorflow.python.ops import gen_random_ops
  from tensorflow.python.ops import gen_resource_variable_ops
  from tensorflow.python.ops import gen_sendrecv_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import list_ops
  from tensorflow.python.ops import logging_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import script_ops
  from tensorflow.python.ops import string_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.ragged import ragged_factory_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.structured import structured_tensor
  from tensorflow.python.platform import test
  from tensorflow.python.saved_model.load import load
  from tensorflow.python.saved_model.save import save
  from tensorflow.python.training import training_ops
  from tensorflow.python.util import compat
  from tensorflow.python.util import nest
  from tensorflow.python.util import tf_decorator
  from tensorflow.python.util import tf_inspect
  
  try:
    import attr  # pylint:disable=g-import-not-at-top
  except ImportError:
    attr = None
  
  
  def total_function_cache(defined):
    # pylint: disable=protected-access
    return (set(defined._function_cache.primary)
            | set(defined._function_cache.arg_relaxed))
    # pylint: enable=protected-access
  
  
  def _example_indexed_slices_with_dense_shape():
    return indexed_slices.IndexedSlices(
        constant_op.constant([1, 2]), constant_op.constant([0, 1]),
        constant_op.constant([2]))
  
  
  def _example_indexed_slices_without_dense_shape():
    return indexed_slices.IndexedSlices(
        constant_op.constant([1, 2]), constant_op.constant([0, 1]))
  
  
  def _spec_for_value(value):
    """Returns the (nested) TypeSpec for a value."""
    if nest.is_sequence(value):
      return nest.map_structure(_spec_for_value, value)
    elif isinstance(value, (ops.Tensor, composite_tensor.CompositeTensor)):
      return type_spec.type_spec_from_value(value)
    else:
      return value
  
  
  # This dummy decorator imitates ordinary decorators utilizing tf_decorator.
  def dummy_tf_decorator(method):
  
    def wrapper(*args, **kwargs):
      return method(*args, **kwargs)
  
    return tf_decorator.make_decorator(method, wrapper)
  
  
  # TODO(mdan): Organize these tests.
  class FunctionTest(test.TestCase, parameterized.TestCase):
  
    def setUp(self):
      super(FunctionTest, self).setUp()
      cpus = config.list_physical_devices('CPU')
      # Set 4 virtual CPUs
      config.set_logical_device_configuration(cpus[0], [
          context.LogicalDeviceConfiguration(),
          context.LogicalDeviceConfiguration(),
          context.LogicalDeviceConfiguration(),
          context.LogicalDeviceConfiguration()
      ])
  
    def testBasic(self):
      matmul = def_function.function(math_ops.matmul)
      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
      sq = matmul(t, t, transpose_a=True)
      sq2 = matmul(sq, t, transpose_a=True)
      self.assertAllEqual(sq.numpy().reshape(-1), [10, 14, 14, 20])
      self.assertAllEqual(sq2.numpy().reshape(-1), [52, 76, 74, 108])
  
    def testPythonFunctionNotCallable(self):
      with self.assertRaisesRegex(TypeError, 'is not a callable object'):
        def_function.function(1)
  
    def testOnExitCallback(self):
      values = []
      def append_1():
        values.append(1)
  
      def append_2():
        values.append(2)
  
      def g(x):
        old_values = list(values)
        ops.add_exit_callback_to_default_func_graph(append_1)
        self.assertEqual(old_values, values)
        return x + 1
  
      tf_g = def_function.function(g)
  
      def f(x):
        old_values = list(values)
        ops.add_exit_callback_to_default_func_graph(append_2)
        self.assertEqual(old_values, values)
        return tf_g(x)
  
      tf_f = def_function.function(f)
      self.assertEmpty(values)
      tf_f(constant_op.constant(1.0))
      self.assertEqual(values, [1, 2])  # Once for g, once for f.
      tf_f(constant_op.constant([1.0]))  # force a retrace
      self.assertEqual(values, [1, 2, 1, 2])  # And again.
  
    def testCannotAddExitCallbackWhenNotInFunctionScope(self):
      with self.assertRaisesRegex(RuntimeError, 'when not building a function.'):
        ops.add_exit_callback_to_default_func_graph(lambda: None)
  
    def testVariable(self):
      v1 = variables.Variable(1.0)
      add = def_function.function(lambda x, v: x + v1 + v)
      v2 = variables.Variable(1.0)
      x = constant_op.constant(1.0)
      r = add(x, v2)
      self.assertEqual(3.0, self.evaluate(r))
  
    def testVariableOnly(self):
      v = variables.Variable(1.0)
      add = def_function.function(lambda x: x.assign_add(1.0))
      r1 = add(v)
      self.assertEqual(2.0, self.evaluate(r1))
      c = constant_op.constant(1.0)
      with self.assertRaisesRegex(AttributeError, 'no attribute'):
        add(c)
  
    @test_util.disable_tfrt('Packed tensor is not supported in tfrt yet.')
    def testPackedVariable(self):
      with ops.device('/cpu:0'):
        v0_0 = resource_variable_ops.ResourceVariable(1.0)
      with ops.device('/cpu:1'):
        v0_1 = resource_variable_ops.ResourceVariable(2.0)
        v1_0 = resource_variable_ops.ResourceVariable(3.0)
      with ops.device('/cpu:2'):
        v1_1 = resource_variable_ops.ResourceVariable(4.0)
  
      packed_var_0 = ops.pack_eager_tensors([v0_0.handle, v0_1.handle])
      packed_var_1 = ops.pack_eager_tensors([v1_0.handle, v1_1.handle])
  
      # TODO(b/145922293): use ResourceVariable.assign_add and
      # ResourceVariable.read_value directly once we support packing multiple
      # ResourceVariable into one ResourceVariable.
      @def_function.function
      def read_var():
        resource_variable_ops.assign_add_variable_op(
            packed_var_0, constant_op.constant(5.0))
        resource_variable_ops.assign_add_variable_op(
            packed_var_1, constant_op.constant(6.0))
        with ops.device('/cpu:0'):
          read0 = resource_variable_ops.read_variable_op(
              packed_var_0, dtype=dtypes.float32)
        with ops.device('/cpu:1'):
          read1 = resource_variable_ops.read_variable_op(
              packed_var_0, dtype=dtypes.float32)
          read2 = resource_variable_ops.read_variable_op(
              packed_var_1, dtype=dtypes.float32)
        with ops.device('/cpu:2'):
          read3 = resource_variable_ops.read_variable_op(
              packed_var_1, dtype=dtypes.float32)
  
        return read0, read1, read2, read3
  
      arg_attrs = read_var.get_concrete_function().function_def.arg_attr
      self.assertLen(arg_attrs, 2)
      self.assertEqual(arg_attrs[0].attr['_composite_device'].s,
                       compat.as_bytes(packed_var_0.device))
      self.assertEqual(arg_attrs[1].attr['_composite_device'].s,
                       compat.as_bytes(packed_var_1.device))
  
      self.assertAllEqual(read_var(), (1 + 5, 2 + 5, 3 + 6, 4 + 6))
  
    def testImplementsAttributeBasic(self):
      v = def_function.function(
          experimental_implements='func')(lambda x, y: x + y)
      with context.graph_mode(), self.cached_session():
        a = array_ops.placeholder(dtypes.float32, ())
        b = array_ops.placeholder(dtypes.float32, ())
        v(a, b)
        gradients_impl.gradients(v(a, b), [a, b])
        fdefs = ops.get_default_graph().as_graph_def().library.function
        self.assertLen(fdefs, 3)
        not_present = 0
        present = 0
        for f in fdefs:
          name = f.signature.name
          if 'forward' in name or 'backward' in name:
            not_present += 1
            self.assertNotIn(function.IMPLEMENTS_ATTRIBUTE_NAME, f.attr, f)
          else:
            present += 1
            self.assertEqual(f.attr[function.IMPLEMENTS_ATTRIBUTE_NAME].s,
                             'func'.encode('ascii'), f)
        self.assertEqual(not_present, 2, fdefs)
        self.assertEqual(present, 1, fdefs)
  
    def testImplementsAttributeAssertsOnSideInput(self):
      with context.graph_mode(), self.cached_session():
        z = array_ops.zeros(0)
        v = def_function.function(
            experimental_implements='func')(lambda x, y: x + y + z)
        a = array_ops.ones((1.0,))
        b = array_ops.ones((1.0,))
        with self.assertRaisesRegex(AssertionError,
                                    'variables are always captured'):
          v(a, b)
        functions = ops.get_default_graph().as_graph_def().library.function
        self.assertEmpty(functions)
  
    def testImplementsAttributeWorksWithGradientTape(self):
      add = lambda x, y: x + y ** 2
      add = def_function.function(experimental_implements='MyFunc')(add)
      x = variables.Variable(3.0)
      y = variables.Variable(2.0)
  
      with backprop.GradientTape() as tape:
        g = add(x, y)
  
      dg_dy, dg_dx = tape.gradient(g, [y, x])
      self.assertEqual(dg_dy.numpy(), 4.0)
      self.assertEqual(dg_dx.numpy(), 1.0)
  
    def testImplementsAttributeWorksOnVariables(self):
      with context.graph_mode(), self.cached_session():
        v = def_function.function(
            experimental_implements='func')(lambda x, y: x + y)
        a = variables.Variable((1.0,))
        b = variables.Variable((1.0,))
        r1 = v(a, b)
        _ = v(a, a)
        functions = ops.get_default_graph().as_graph_def().library.function
        # Verify that we created only one function
        self.assertLen(functions, 1)
        # Verify that eval() reads the current values.
        a.initializer.run()
        b.initializer.run()
        self.assertEqual(r1.eval(), 2)
  
        a.assign_add([1]).eval()
        self.assertEqual(r1.eval(), 3)
  
    def testImplementsAttributeWorksOnConstants(self):
      with context.graph_mode(), self.cached_session():
        v = def_function.function(
            experimental_implements='func')(lambda x, y: x + y)
        a = variables.Variable(1.0)
        r1 = v(a, 2.)
        r2 = v(2., a)
        functions = ops.get_default_graph().as_graph_def().library.function
        self.assertLen(functions, 1)
        self.assertLen(functions[0].signature.input_arg, 2)
        # Verify that eval() reads the current values.
        a.initializer.run()
        self.assertEqual(r1.eval(), 3)
        self.assertEqual(r2.eval(), 3)
  
    def testImplementsAttributeSpecializes(self):
      with context.graph_mode(), self.cached_session():
        v = def_function.function(
            experimental_implements='func')(lambda x, y: x + y)
        a = variables.Variable(1.0)
        r1 = v(a, [2.])
        r2 = v([2., 2], a)
        functions = ops.get_default_graph().as_graph_def().library.function
        self.assertLen(functions, 2)
        # Ensure that all parameters are still there and haven't been inlined!
  
        self.assertLen(functions[0].signature.input_arg, 2)
        self.assertLen(functions[1].signature.input_arg, 2)
        # Verify that eval() reads the current values.
        a.initializer.run()
        numpy.testing.assert_equal(r1.eval(), [3.])
        numpy.testing.assert_equal(r2.eval(), [3., 3.])
  
    def testImplementsWorksWithTensorSpec(self):
      v = def_function.function(
          experimental_implements='func')(lambda x, y: x + y)
      v = v.get_concrete_function(
          tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32),
          tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32))
      x = v(1., 2.)
      self.assertEqual(x.numpy(), 3.)
  
    def testImplementsAttributeAsNameAttrList(self):
      implements_attr = (
          'name: "embedding_matmul" attr {   key: "key1"   value {     i: 2   } '
          '} attr {   key: "key2"   value {     b: false   } }')
      v = def_function.function(
          experimental_implements=implements_attr)(lambda x, y: x + y)
      with context.graph_mode(), self.cached_session():
        a = array_ops.placeholder(dtypes.float32, ())
        b = array_ops.placeholder(dtypes.float32, ())
        v(a, b)
        gradients_impl.gradients(v(a, b), [a, b])
        fdefs = ops.get_default_graph().as_graph_def().library.function
        self.assertLen(fdefs, 3)
        not_present = 0
        present = 0
        for f in fdefs:
          name = f.signature.name
          if 'forward' in name or 'backward' in name:
            not_present += 1
            self.assertNotIn(function.IMPLEMENTS_ATTRIBUTE_NAME, f.attr, f)
          else:
            present += 1
            attr_value = f.attr[function.IMPLEMENTS_ATTRIBUTE_NAME]
            self.assertIsNotNone(attr_value.func, f)
            self.assertEqual(attr_value.func.name, 'embedding_matmul')
            name_attrs = attr_value.func.attr
            self.assertLen(name_attrs, 2)
        self.assertEqual(not_present, 2, fdefs)
        self.assertEqual(present, 1, fdefs)
  
    def testExternalControlDependency(self):
      with ops.Graph().as_default(), self.test_session():
        v = variables.Variable(1.0)
        v.initializer.run()
  
        op = v.assign_add(1.0)
  
        @function.defun
        def f():
          with ops.control_dependencies([op]):
            return 1.0
  
        self.evaluate(f())
        self.assertAllEqual(self.evaluate(v), 2.0)
  
    def testInputShapeFunctionRelaxation(self):
      unknown_dim = [False]
  
      @function.defun(experimental_relax_shapes=True)
      def func(a):
        if a._shape_tuple()[0] is None:
          unknown_dim[0] = True
        return a + 1
  
      func(constant_op.constant([]))
      self.assertFalse(unknown_dim[0])
      self.assertLen(total_function_cache(func), 1)
  
      func(constant_op.constant([1.0]))
      self.assertFalse(unknown_dim[0])
      self.assertLen(total_function_cache(func), 2)
  
      func(constant_op.constant([1.0, 2.0]))
      self.assertTrue(unknown_dim[0])
      self.assertLen(total_function_cache(func), 2)
  
    def testInputShapeRelaxationOnInstanceMethod(self):
      # Test that experimental_relax_shapes is passed during
      # instance method bounding.
      unknown_dim = [False]
  
      class Foo(object):
  
        @def_function.function(experimental_relax_shapes=True)
        def func(self, a):
          if a._shape_tuple()[0] is None:
            unknown_dim[0] = True
          return a + 1
  
      foo = Foo()
      foo.func(constant_op.constant([]))
      self.assertFalse(unknown_dim[0])
  
      foo.func(constant_op.constant([1.0]))
      self.assertFalse(unknown_dim[0])
  
      foo.func(constant_op.constant([1.0, 2.0]))
      self.assertTrue(unknown_dim[0])
  
    def testInputShapeFunctionRelaxationWithRaggedTensors(self):
      traced_type_spec = [None]
  
      @def_function.function(experimental_relax_shapes=True)
      def func(x):
        traced_type_spec[0] = x._type_spec
        return x
  
      def check_trace(x, expected_trace):
        traced_type_spec[0] = None
        func(x)
        self.assertEqual(traced_type_spec[0], expected_trace)
  
      check_trace(  # Initial call gets traced.
          ragged_factory_ops.constant([[1], [2, 3, 4]]),
          ragged_tensor.RaggedTensorSpec([2, None], dtypes.int32))
      check_trace(  # Input TypeSpec is the same -> no retrace.
          ragged_factory_ops.constant([[1, 2], [3, 4]]), None)
      check_trace(  # Even if component tensor shapes change -> no retrace.
          ragged_factory_ops.constant([[1, 2], [3, 4, 5, 6]]), None)
      check_trace(  # Different TypeSpec shape (nrows): retrace
          ragged_factory_ops.constant([[1], [2], [3]]),
          ragged_tensor.RaggedTensorSpec([3, None], dtypes.int32))
      check_trace(  # Different nrows again: relax & retrace
          ragged_factory_ops.constant([[1], [2], [3], [4]]),
          ragged_tensor.RaggedTensorSpec([None, None], dtypes.int32))
      check_trace(  # Different nrows yet again: not retrace
          ragged_factory_ops.constant([[1]]), None)
      check_trace(  # Different ragged_rank: retrace
          ragged_factory_ops.constant([[[1]]]),
          ragged_tensor.RaggedTensorSpec([1, None, None], dtypes.int32))
      check_trace(  # Different ragged_rank again: retrace & relax
          ragged_factory_ops.constant([[[1]], [[2]]]),
          ragged_tensor.RaggedTensorSpec([None, None, None], dtypes.int32))
  
    def testInputShapeFunctionRelaxationWithStructuredTensors(self):
      traced_type_spec = [None]
  
      @def_function.function(experimental_relax_shapes=True)
      def func(x):
        traced_type_spec[0] = x._type_spec
        return x
  
      def check_trace(x, expected_trace):
        traced_type_spec[0] = None
        func(x)
        self.assertEqual(traced_type_spec[0], expected_trace)
  
      # If we have TypeSpecs that differ in ways other than just their shape,
      # then retrace each time.
      check_trace(
          structured_tensor.StructuredTensor.from_pyval({'a': [1]}),
          structured_tensor.StructuredTensorSpec(
              [], {'a': tensor_spec.TensorSpec((1,), dtypes.int32)}))
      check_trace(
          structured_tensor.StructuredTensor.from_pyval({'b': [1]}),
          structured_tensor.StructuredTensorSpec(
              [], {'b': tensor_spec.TensorSpec((1,), dtypes.int32)}))
      check_trace(
          structured_tensor.StructuredTensor.from_pyval({'c': [1]}),
          structured_tensor.StructuredTensorSpec(
              [], {'c': tensor_spec.TensorSpec((1,), dtypes.int32)}))
  
      # But if we call again with only shape different, then do relax:
      check_trace(  # retrace
          structured_tensor.StructuredTensor.from_pyval({'a': [1, 2]}),
          structured_tensor.StructuredTensorSpec(
              [], {'a': tensor_spec.TensorSpec((2,), dtypes.int32)}))
      check_trace(  # relax & retrace
          structured_tensor.StructuredTensor.from_pyval({'a': [1, 2, 3]}),
          structured_tensor.StructuredTensorSpec(
              [], {'a': tensor_spec.TensorSpec((None,), dtypes.int32)}))
      check_trace(  # use relaxed graph
          structured_tensor.StructuredTensor.from_pyval({'a': [1, 2, 3, 4]}),
          None)
  
    def testInputShapeFunctionRelaxationWithDatasetIterators(self):
      # For dataset iterators, the TypeSpec includes type information that's
      # not derivable from the component tensors.  Make sure that the TypeSpec
      # shapes get relaxed as appropriate.
  
      traced_type_spec = [None]
  
      @def_function.function(experimental_relax_shapes=True)
      def func(x):
        traced_type_spec[0] = x._type_spec
        return x
  
      def check_trace(x, expected_trace):
        traced_type_spec[0] = None
        func(x)
        self.assertEqual(traced_type_spec[0], expected_trace)
  
      ds_1_2 = dataset_ops.DatasetV2.from_tensors(array_ops.zeros([1, 2]))
      ds_2_2 = dataset_ops.DatasetV2.from_tensors(array_ops.zeros([2, 2]))
      ds_3_2 = dataset_ops.DatasetV2.from_tensors(array_ops.zeros([3, 2]))
      ds_4_2 = dataset_ops.DatasetV2.from_tensors(array_ops.zeros([4, 2]))
      ds_2_1 = dataset_ops.DatasetV2.from_tensors(array_ops.zeros([2, 1]))
      check_trace(  # shape=[1, 2]: retrace
          dataset_ops.make_one_shot_iterator(ds_1_2),
          iterator_ops.IteratorSpec(
              tensor_spec.TensorSpec([1, 2], dtypes.float32)))
      check_trace(  # shape=[1, 2]: no retrace (use the [1, 2] graph)
          dataset_ops.make_one_shot_iterator(ds_1_2), None)
      check_trace(  # shape=[2, 2]: retrace
          dataset_ops.make_one_shot_iterator(ds_2_2),
          iterator_ops.IteratorSpec(
              tensor_spec.TensorSpec([2, 2], dtypes.float32)))
      check_trace(  # shape=[3, 2]: relax to [None, 2] and retrace
          dataset_ops.make_one_shot_iterator(ds_3_2),
          iterator_ops.IteratorSpec(
              tensor_spec.TensorSpec([None, 2], dtypes.float32)))
      check_trace(  # shape=[4, 2]: no retrace (use the [None, 2] graph)
          dataset_ops.make_one_shot_iterator(ds_4_2), None)
      check_trace(  # shape=[2, 1]: relax to [None, None] and retrace
          dataset_ops.make_one_shot_iterator(ds_2_1),
          iterator_ops.IteratorSpec(
              tensor_spec.TensorSpec([None, None], dtypes.float32)))
  
    def testCapturesVariables(self):
      a = variables.Variable(1.0, trainable=False)
      b = variables.Variable(1.0)
      cc = [None]
  
      @def_function.function
      def f():
        c = cc[0]
        if c is None:
          c = cc[0] = variables.Variable(1.)
        return a + b + c + 1
  
      cf = f.get_concrete_function()
      c = cc[0]
  
      captured_variables = {v.ref() for v in (a, b, c)}
      trainable_variables = {v.ref() for v in (b, c)}
      self.assertEqual({v.ref() for v in cf.variables}, captured_variables)
      self.assertEqual({v.ref() for v in cf.trainable_variables},
                       trainable_variables)
      self.assertEqual(cf.variables, cf.graph.variables)
      self.assertEqual(cf.trainable_variables, cf.graph.trainable_variables)
  
    def testNestedInputShapeFunctionRelaxation(self):
      unknown_dim = [False]
  
      @function.defun(experimental_relax_shapes=True)
      def func(a_, b_=None):
        del a_  # Only used to check which cache is used.
        self.assertEqual(b_[0]._shape_tuple(), ())
        if b_[1]._shape_tuple()[0] is None:
          unknown_dim[0] = True
        return b_[0] + 1
  
      a = 'hi'
      b0 = constant_op.constant(1.0)
      func(a, b_=[b0, constant_op.constant([])])
      self.assertFalse(unknown_dim[0])
      self.assertLen(total_function_cache(func), 1)
  
      func(a, b_=[b0, constant_op.constant([1.0])])
      self.assertFalse(unknown_dim[0])
      self.assertLen(total_function_cache(func), 2)
  
      func(a, b_=[b0, constant_op.constant([1.0, 1.0])])
      self.assertTrue(unknown_dim[0])
      self.assertLen(total_function_cache(func), 2)
  
      unknown_dim[0] = False
  
      # Now do the same except with a new a which is not a tensor; this should
      # change the cache key.
      a = 'bye'
      func(a, b_=[b0, constant_op.constant([])])
      self.assertFalse(unknown_dim[0])
      self.assertLen(total_function_cache(func), 3)
  
      # Since we already marked a cache miss for a function with the same
      # non-input signatures, here we will immediately start relaxing shapes.
      func(a, b_=[b0, constant_op.constant([1.0])])
      self.assertTrue(unknown_dim[0])
      self.assertLen(total_function_cache(func), 3)
  
    def testNestedShapeFunctionRelaxation(self):
  
      got_shape = [None]
  
      # The inner function will go through shape relaxation because the shapes it
      # receives will be [1], [2], [3], ...
      @def_function.function(experimental_relax_shapes=True)
      def bar(x_shape):
        got_shape[0] = x_shape._shape_tuple()
        return x_shape
  
      # The outer function will not go through shape relaxation because the shapes
      # it receives will be [1], [[1]], [[[1]]], ...
      @def_function.function(experimental_relax_shapes=True)
      def foo(ones):
        return bar(array_ops.shape(ones))
  
      for rank in range(1, 6):
        x_shape = self.evaluate(foo(array_ops.ones([1] * rank)))
        self.assertAllEqual(x_shape, [1] * rank)
        if rank < 3:
          self.assertEqual(got_shape[0], (rank,))
        else:
          self.assertEqual(got_shape[0], (None,))
  
    def testNoHash(self):
  
      @def_function.function()
      def f(_):
        return 1.0
  
      with self.assertRaisesRegex(ValueError, r'got.*set'):
        f(set([]))
  
    def testFuncName(self):
  
      @function.defun_with_attributes(attributes={'func_name': 'multiply'})
      def add(x, y):
        _ = x * y
        return x + y
  
      @function.defun
      def add_2(x, y):
        _ = x * y
        return x + y
  
      self.assertEqual(add._name, 'multiply')
      self.assertEqual(add_2._name, 'add_2')
  
    def testBasicGraphMode(self):
      matmul = def_function.function(math_ops.matmul)
  
      @def_function.function
      def sq(a):
        return matmul(a, a)
  
      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
      out = sq(t)
      self.assertAllEqual(out, math_ops.matmul(t, t).numpy())
  
    def testNestedInputsGraphMode(self):
      matmul = def_function.function(math_ops.matmul)
  
      pair = collections.namedtuple('pair', ['a', 'b'])
  
      @def_function.function
      def a_times_b(inputs):
        return matmul(inputs.a['a'], inputs.b['b'])
  
      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
  
      out = a_times_b(pair({'a': t}, {'b': t}))
      self.assertAllEqual(out, math_ops.matmul(t, t).numpy())
  
    def testNestedOutputsGraphMode(self):
      matmul = def_function.function(math_ops.matmul)
  
      pair = collections.namedtuple('pair', ['a', 'b'])
  
      @def_function.function()
      def pairs_mul(pair_a, pair_b):
        return pair(matmul(pair_a.a, pair_b.a), matmul(pair_a.b, pair_b.b))
  
      a = constant_op.constant([[1.0, 2.0], [1.0, 2.0]])
      b = constant_op.constant([[3.0, 4.0], [3.0, 4.0]])
  
      out = pairs_mul(pair(a, b), pair(b, a))
      expected = pair(math_ops.matmul(a, b).numpy(),
                      math_ops.matmul(b, a).numpy())
      self.assertAllClose(out, expected)
  
    @parameterized.named_parameters(
        dict(testcase_name='Defun',
             function_decorator=function.defun),
        dict(testcase_name='DefFunction',
             function_decorator=def_function.function))
    def testNestedFunctionGraphNotOutOfDate(self, function_decorator):
      @function_decorator
      def f():
        return constant_op.constant(1.)
  
      class _Model(object):
  
        @function_decorator
        def g(self):
          self.f = f.get_concrete_function()
  
      model = _Model()
      model.g()
      concrete = model.f
      weak_g_graph = weakref.ref(model.g.get_concrete_function().graph)
      self.assertIs(weak_g_graph(), concrete.graph.outer_graph)
      weak_g = weakref.ref(model.g)
      del model
      self.assertIsNone(weak_g())
      self.assertIsNone(weak_g_graph())
      self.assertIsNotNone(concrete.graph.outer_graph)
      self.assertIs(ops.get_default_graph(), concrete.graph.outer_graph)
  
    def testGraphEagerIsolation(self):
  
      @function.defun
      def f():
        self.v = variables.Variable(1.0)
        return self.v.read_value()
  
      self.assertAllEqual(f(), 1.0)
  
      with ops.Graph().as_default():
        self.assertEqual(f().shape, ())
  
    def testBasicGraphFunction(self):
      matmul = def_function.function(math_ops.matmul)
  
      @def_function.function
      def sq(a):
        return matmul(a, a)
  
      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
  
      sq_op = sq.get_concrete_function(t)
      self.assertEqual(sq_op.output_shapes, tensor_shape.TensorShape([2, 2]))
      out = sq_op(t)
      self.assertAllEqual(out, math_ops.matmul(t, t).numpy())
  
    def testGetConcreteFunctionThreadSafety(self):
  
      @def_function.function
      def sq():
        t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
        return math_ops.matmul(t, t)
  
      concrete_functions = []
  
      def thread_func(_):
        cf = sq.get_concrete_function()
        concrete_functions.append(cf)
  
      num_threads = 100
      pool = multiprocessing.pool.ThreadPool(num_threads)
      _ = pool.map(thread_func, list(range(num_threads)))
  
      self.assertLen(set(concrete_functions), 1)
  
    def testGetConcreteFunctionThreadSafetyWithArgs(self):
      @def_function.function
      def add_100(*args):
        return math_ops.add_n(args)
  
      p = multiprocessing.pool.ThreadPool(2)
      args = (constant_op.constant(1.),) * 100
      f1, f2 = p.map(add_100.get_concrete_function, [args] * 2)
      # I see about len(args) + max(0, len(args) - 3) arguments expected.
      f1(*args)
      del f2
  
    def testInputSpecGraphFunction(self):
      matmul = def_function.function(math_ops.matmul)
  
      @def_function.function
      def sq(a):
        return matmul(a, a)
  
      sq_op = sq.get_concrete_function(
          tensor_spec.TensorSpec((None, None), dtypes.float32))
      self.assertEqual([None, None], sq_op.output_shapes.as_list())
  
      t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
      out1 = sq_op(t1)
      self.assertAllEqual(out1, math_ops.matmul(t1, t1).numpy())
  
      t2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
      out2 = sq_op(t2)
      self.assertAllEqual(out2, math_ops.matmul(t2, t2).numpy())
  
    def testNestedInputSpecGraphFunction(self):
      matmul = def_function.function(math_ops.matmul)
  
      @def_function.function
      def sq(mats):
        ((a, b),) = mats
        return matmul(a, b)
  
      sq_op_autonamed = sq.get_concrete_function(
          [(tensor_spec.TensorSpec((None, None), dtypes.float32),
            tensor_spec.TensorSpec((None, None), dtypes.float32))])
      self.assertEqual([None, None], sq_op_autonamed.output_shapes.as_list())
  
      sq_op = sq.get_concrete_function(
          [(tensor_spec.TensorSpec((None, None), dtypes.float32,
                                   name='first_mat'),
            tensor_spec.TensorSpec((None, None), dtypes.float32,
                                   name='second_mat'))])
      self.assertEqual([None, None], sq_op.output_shapes.as_list())
  
      t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
      t2 = constant_op.constant([[1.4, 2.4], [3.4, 4.4]])
      out = sq_op(first_mat=t1, second_mat=t2)
      self.assertAllEqual(out, math_ops.matmul(t1, t2).numpy())
      self.assertAllEqual(sq_op_autonamed(t1, t2),
                          math_ops.matmul(t1, t2).numpy())
  
    def testExecutingStatelessDefunConcurrently(self):
  
      @def_function.function
      def stateless(x):
        return math_ops.multiply(2.0, x)
  
      pool = multiprocessing.pool.ThreadPool()
      inputs = [constant_op.constant(1.0 * x) for x in range(100)]
      outputs = [float(out) for out in pool.map(stateless, inputs)]
      expected = [float(2.0 * x) for x in inputs]
      self.assertSequenceEqual(outputs, expected)
  
    def testExecutingManyStatelessDefunsConcurrently(self):
  
      @def_function.function
      def stateless(x):
        del x
        return math_ops.multiply(2.0, 2.0)
  
      pool = multiprocessing.pool.ThreadPool()
      # `pool.map` below instantiates 100 functions, one for each object.
      objects = [object() for _ in range(100)]
      outputs = [float(out) for out in pool.map(stateless, objects)]
      expected = [4.0] * 100
      self.assertSequenceEqual(outputs, expected)
  
    @test_util.disable_tfrt('b/169431085: This test is flaky on tfrt')
    def testExecutingStatefulDefunConcurrently(self):
  
      v = resource_variable_ops.ResourceVariable(1.0)
  
      @def_function.function
      def stateful(x):
        v.assign(x)
  
      pool = multiprocessing.pool.ThreadPool()
      inputs = [constant_op.constant(0.0)] * 100
      pool.map(stateful, inputs)
      self.assertEqual(float(v.read_value()), 0.0)
  
    def testExecutingManyStatefulDefunsConcurrently(self):
  
      v = resource_variable_ops.ResourceVariable(1.0)
  
      @def_function.function
      def stateful(x):
        del x
        return v.assign(0.0)
  
      pool = multiprocessing.pool.ThreadPool()
      # `pool.map` below instantiates 100 functions, one for each object.
      pool.map(stateful, [object() for _ in range(100)])
      self.assertEqual(float(v.read_value()), 0.0)
  
    def testShareRendezvous(self):
  
      # Disable grappler from inlining the functions. Note we run the send & recv
      # in graph mode since with eager mode the function should automatically be
      # inlined.
      context.context().set_optimizer_experimental_options(
          {'disable_meta_optimizer': True})
  
      cpu = '/device:CPU:0'
  
      signature = [tensor_spec.TensorSpec([], dtypes.int32)]
  
      @def_function.function
      def send():
        x = constant_op.constant(1)
        gen_sendrecv_ops.send(x, 'x', cpu, 0, cpu)
        return x
  
      send._shared_rendezvous = True  # pylint: disable=protected-access
  
      @def_function.function(input_signature=signature)
      def send_body(n):
        send()
        return n - 1
  
      @def_function.function
      def recv():
        return gen_sendrecv_ops.recv(dtypes.int32, 'x', cpu, 0, cpu)
  
      recv._shared_rendezvous = True  # pylint: disable=protected-access
  
      @def_function.function(input_signature=signature)
      def recv_body(n):
        recv()
        return n - 1
  
      @def_function.function(input_signature=signature)
      def cond(n):
        return n > 0
  
      # Instead of calling the send & recv functions directly we want to call them
      # through a functional while to ensure the rendezvous is shared across the
      # while boundary.
      @def_function.function
      def fn(n):
        functional_ops.While([n], cond.get_concrete_function(),
                             send_body.get_concrete_function())
        return functional_ops.While([n], cond.get_concrete_function(),
                                    recv_body.get_concrete_function())
  
      # Use a graph context since functions will not be automatically inlined
      with context.graph_mode(), self.cached_session():
        self.evaluate(fn(2))
  
    def disabled_testRandomSeed(self):
  
      @def_function.function
      def f():
        return random_ops.random_normal(())
  
      random_seed.set_random_seed(1)
      x = f()
      self.assertNotEqual(x, f())
      random_seed.set_random_seed(1)
      self.assertAllEqual(f(), x)
  
    def testNestedInputsGraphFunction(self):
      matmul = def_function.function(math_ops.matmul)
  
      pair = collections.namedtuple('pair', ['a', 'b'])
  
      @def_function.function
      def a_times_b(inputs):
        return matmul(inputs.a['a'], inputs.b['b'])
  
      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
      sq_op = a_times_b.get_concrete_function(
          pair(dict(a=tensor_spec.TensorSpec([2, 2], dtypes.float32, 'a')),
               dict(b=tensor_spec.TensorSpec([2, 2], dtypes.float32, 'b'))))
      self.assertEqual(sq_op.output_shapes, tensor_shape.TensorShape([2, 2]))
      out = sq_op(a=t, b=t)
      self.assertAllEqual(out, math_ops.matmul(t, t).numpy())
  
    def testNestedOutputGraphFunction(self):
      matmul = def_function.function(math_ops.matmul)
  
      @def_function.function
      def sq(a):
        return (matmul(a, a), {'b': constant_op.constant(1.0)})
  
      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
  
      sq_op = sq.get_concrete_function(t)
      self.assertEqual(sq_op.output_shapes,
                       (tensor_shape.TensorShape([2, 2]),
                        {'b': tensor_shape.TensorShape([])}))
      self.assertEqual(sq_op.output_dtypes,
                       (dtypes.float32, {'b': dtypes.float32}))
      (a, b) = sq_op(t)
      self.assertAllEqual(a, math_ops.matmul(t, t).numpy())
      self.assertAllEqual(b['b'].numpy(), 1.0)
  
    def testGraphFunctionNoneOutput(self):
      @def_function.function
      def fn(unused_a, unused_b):
        return None
  
      x = constant_op.constant(1)
      fn_op = fn.get_concrete_function(x, x)
      self.assertEqual(fn_op.output_dtypes, None)
      self.assertEqual(fn_op.output_shapes, None)
      self.assertAllEqual(fn_op(x, x), None)
  
    def testDefunNumpyArraysConvertedToTensors(self):
  
      def f(x):
        self.assertIsInstance(x, ops.Tensor)
        return x
  
      x = random_ops.random_uniform([2, 2]).numpy()
      defined = function.defun(f)
      defined(x)
      self.assertLen(total_function_cache(defined), 1)
  
      x = random_ops.random_uniform([2, 2]).numpy()
      defined(x)
      # A NumPy array with different values but the same shape and dtype
      # shouldn't trigger another function definition.
      self.assertLen(total_function_cache(defined), 1)
  
      np_ones = numpy.ones([], numpy.float32)
      np_zeros = numpy.zeros([], numpy.float32)
      tf_ones = array_ops.ones([])
      tf_zeros = array_ops.zeros([])
  
      # Test that the numpy array is properly an argument to the graph function.
      self.assertEqual(1., defined(np_ones).numpy())
      self.assertLen(total_function_cache(defined), 2)
      self.assertEqual(0., defined(np_zeros).numpy())
      self.assertEqual(1., defined(tf_ones).numpy())
      self.assertEqual(0., defined(tf_zeros).numpy())
      self.assertLen(total_function_cache(defined), 2)
  
      # Test that mutable inputs are supported.
      mutable = numpy.ones([], numpy.float32)
      self.assertEqual(1., defined(mutable).numpy())
      mutable.fill(0)
      self.assertEqual(0., defined(mutable).numpy())
  
      class MyNdarray(numpy.ndarray):
        pass
  
      # Test that the subclasses of ndarray are converted too.
      self.assertEqual(1., defined(np_ones.view(MyNdarray)).numpy())
      self.assertEqual(0., defined(np_zeros.view(MyNdarray)).numpy())
  
      # We should not have triggered any re-tracing of the python function.
      self.assertLen(total_function_cache(defined), 2)
  
    def testNumpyDtypeInputSupported(self):
      @function.defun
      def f(x, dtype):
        return constant_op.constant(dtype(x))
  
      self.assertEqual(f(1, numpy.float32).numpy(), numpy.float32(1))
      self.assertEqual(f(2, numpy.float32).numpy(), numpy.float32(2))
      self.assertEqual(f(1, numpy.int32).numpy(), numpy.int32(1))
      self.assertEqual(f(2, numpy.int32).numpy(), numpy.int32(2))
  
    def testDefunNumpyArraysConvertedToTensorsInKwargs(self):
  
      def f(**kwargs):
        x = kwargs.pop('x')
        self.assertIsInstance(x, ops.Tensor)
        return x
  
      x = random_ops.random_uniform([2, 2]).numpy()
      defined = function.defun(f)
      defined(x=x)
      self.assertLen(total_function_cache(defined), 1)
  
      x = random_ops.random_uniform([2, 2]).numpy()
      defined(x=x)
      # A NumPy array with different values but the same shape and dtype
      # shouldn't trigger another function definition.
      self.assertLen(total_function_cache(defined), 1)
  
      # Test that the numpy array is properly an argument to the graph function.
      self.assertEqual(1., defined(x=numpy.ones([])).numpy())
      self.assertEqual(0., defined(x=numpy.zeros([])).numpy())
      self.assertEqual(1., defined(x=array_ops.ones([])).numpy())
      self.assertEqual(0., defined(x=array_ops.zeros([])).numpy())
  
    def testDefunCapturedInt32(self):
      x = constant_op.constant(1, dtype=dtypes.int32)
  
      @def_function.function
      def add_int32s():
        return x + x
  
      self.assertEqual(2, int(add_int32s()))
  
    def testDefunReadVariable(self):
      v = resource_variable_ops.ResourceVariable(1.0)
  
      @def_function.function
      def f():
        return v.read_value()
  
      self.assertEqual(1.0, float(f()))
  
    def testDefunAssignAddVariable(self):
      v = resource_variable_ops.ResourceVariable(1.0)
      x = constant_op.constant(2.0)
  
      @def_function.function
      def test_assign_add():
        v.assign_add(x)
        return v.read_value()
  
      self.assertEqual(3.0, float(test_assign_add()))
  
    @test_util.run_in_graph_and_eager_modes
    def testTensorInitializationInFunctionRaisesError(self):
  
      @def_function.function
      def tensor_init():
        with self.assertRaisesRegex(ValueError, 'could not be lifted out'):
          resource_variable_ops.ResourceVariable(constant_op.constant(2.0))
  
      tensor_init()
  
    @test_util.run_in_graph_and_eager_modes
    def testCallableTensorInitializationInFunction(self):
  
      @def_function.function
      def tensor_init():
        self.v = resource_variable_ops.ResourceVariable(
            lambda: constant_op.constant(2.0))
        return self.v.read_value()
  
      value = tensor_init()
      if not context.executing_eagerly():
        self.evaluate(variables.global_variables_initializer())
      self.assertEqual(self.evaluate(value), 2.0)
  
    @test_util.also_run_as_tf_function
    def testInitScopeTensorInitializationInFunction(self):
  
      @def_function.function
      def tensor_init():
        with ops.init_scope():
          const = constant_op.constant(2.0)
        # Note: this variable bypasses tf.function's variable creation
        # requirements by bypassing variable_creator_scope by using
        # ResourceVariable instead of Variable.
        self.v = resource_variable_ops.ResourceVariable(const)
        return self.v.read_value()
  
      value = tensor_init()
      self.assertAllEqual(value, 2.0)
  
    @test_util.run_in_graph_and_eager_modes
    def testGetConcreteFunctionCreatesVariables(self):
  
      v_holder = []
  
      @def_function.function
      def tensor_init():
        if not v_holder:
          v_holder.append(variables.Variable(5.))
        return v_holder[0].read_value()
  
      concrete = tensor_init.get_concrete_function()
      self.evaluate(variables.global_variables_initializer())
      self.assertAllEqual(5., self.evaluate(concrete()))
      self.assertAllEqual(5., self.evaluate(tensor_init()))
  
    def testFuncGraphCaptureByValue(self):
      v = variables.Variable(1.0)
  
      def trivial_function():
        return v.read_value()
  
      graph_function = function.Function(
          trivial_function, 'test', capture_by_value=True)
  
      self.assertAllEqual(graph_function(), 1.0)
      v.assign(2.0)
      self.assertAllEqual(graph_function(), 1.0)
  
    def testFuncGraphCaptureByValueNested(self):
      v = variables.Variable(1.0)
  
      def trivial_function():
        return control_flow_ops.cond(
            array_ops.placeholder_with_default(True, ()),
            v.read_value, v.read_value)
  
      graph_function = function.Function(
          trivial_function, 'test', capture_by_value=True)
  
      self.assertAllEqual(graph_function(), 1.0)
      v.assign(2.0)
      self.assertAllEqual(graph_function(), 1.0)
  
    def testDefunShapeInferenceWithCapturedResourceVariable(self):
      v = resource_variable_ops.ResourceVariable([[1, 2], [3, 4]])
  
      def f():
        x = constant_op.constant([[1, 2], [3, 4]])
        out = math_ops.matmul(v, x)
        self.assertEqual(out.shape, tensor_shape.TensorShape([2, 2]))
        # We do not return v directly since the tensor conversion function of
        # ResourceVariable returns the read value and not the resource itself.
        return v._handle
  
      compiled = def_function.function(f)
      var_handle = compiled()
      self.assertEqual(var_handle.dtype, dtypes.resource)
      self.assertEqual(var_handle.shape, tensor_shape.TensorShape([]))
      var_t = resource_variable_ops.read_variable_op(var_handle, dtype=v.dtype)
      self.assertEqual(var_t.shape, tensor_shape.TensorShape([2, 2]))
  
    def testShapeInferenceForMoreSpecificInput(self):
  
      def f(a):
        return array_ops.reshape(a, [-1, 3])
  
      signature = [tensor_spec.TensorSpec(None, dtypes.float32)]
      compiled = def_function.function(f, input_signature=signature)
  
      @def_function.function
      def use_f():
        inputs = array_ops.zeros([10, 10, 3])
        self.assertAllEqual(f(inputs).shape, compiled(inputs).shape)
  
      use_f()
  
    def testFuncListAttr(self):
  
      @function.defun
      def test_function(val):
  
        def fn1():
          return array_ops.ones([10])
  
        fn2 = lambda: array_ops.ones([10]) * 2
  
        def fn3(x=3):
          return array_ops.ones([10]) * x
        fn4 = functools.partial(fn3, x=4)
        fn5 = functools.partial(fn3, 5)
  
        return gen_functional_ops.case(val, [], [dtypes.float32],
                                       [function.defun(f).get_concrete_function()
                                        for f in (fn1, fn2, fn3, fn4, fn5)])
  
      ones = array_ops.ones([10])
      self.assertAllEqual([ones], test_function(0))
      self.assertAllEqual([ones * 2], test_function(1))
      self.assertAllEqual([ones * 3], test_function(2))
      self.assertAllEqual([ones * 4], test_function(3))
      self.assertAllEqual([ones * 5], test_function(4))
      self.assertAllEqual([ones * 5], test_function(22))  # default branch
  
    @test_util.enable_control_flow_v2
    def testVariableInLoopInFunction(self):
  
      @function.defun
      def test_function():
  
        def loop_test(_):
          return False
  
        def loop_body(_):
          return variable_scope.get_variable('a', shape=())
  
        return control_flow_ops.while_loop(loop_test, loop_body, [0.0])
  
      self.assertEqual(test_function().shape, [])
  
    def testDefunShapeInferenceWithCapturedResourceVariableInGraphMode(self):
      with context.graph_mode():
        v = resource_variable_ops.ResourceVariable([[1, 2], [3, 4]])
  
        def f():
          x = constant_op.constant([[1, 2], [3, 4]])
          out = math_ops.matmul(v, x)
          self.assertEqual(out.shape, tensor_shape.TensorShape([2, 2]))
          # We do not return v directly since the tensor conversion function of
          # ResourceVariable returns the read value and not the resource itself.
          return v._handle
  
        compiled = def_function.function(f)
        var_handle = compiled()
        self.assertEqual(var_handle.dtype, dtypes.resource)
        self.assertEqual(var_handle.shape, tensor_shape.TensorShape([]))
        var_t = resource_variable_ops.read_variable_op(var_handle, dtype=v.dtype)
        self.assertEqual(var_t.shape, tensor_shape.TensorShape([2, 2]))
  
    def testDefunShapeInferenceWithCapturedVariableInGraphMode(self):
      with context.graph_mode():
        v = variables.Variable([[1, 2], [3, 4]])
  
        def f():
          x = constant_op.constant([[1, 2], [3, 4]])
          out = math_ops.matmul(v, x)
          self.assertEqual(out.shape, tensor_shape.TensorShape([2, 2]))
  
        # Check that shape inference works while creating the defun
        compiled = def_function.function(f)
        compiled()
  
    def testDefunShapeInferenceWithCapturedTensorListInGraphMode(self):
      with context.graph_mode():
        tensor_list = list_ops.empty_tensor_list(
            element_dtype=dtypes.float32,
            element_shape=ops.convert_to_tensor([], dtype=dtypes.int32))
        tensor_list = list_ops.tensor_list_push_back(tensor_list,
                                                     constant_op.constant(1.0))
        tensor_list = list_ops.tensor_list_push_back(tensor_list,
                                                     constant_op.constant(2.0))
  
        def f():
          tl, value = list_ops.tensor_list_pop_back(
              tensor_list, element_dtype=dtypes.float32)
          self.assertEqual(value.shape, tensor_shape.TensorShape([]))
          return tl
  
        compiled = def_function.function(f)
        output_tensor_list = compiled()
        _, value = list_ops.tensor_list_pop_back(
            output_tensor_list, element_dtype=dtypes.float32)
        self.assertEqual(value.shape, tensor_shape.TensorShape([]))
  
    @test_util.run_in_graph_and_eager_modes
    def testDefunForcesResourceVariables(self):
  
      def variable_creator():
        self.v = variables.Variable(0.0)
        return self.v.read_value()
  
      self.v = None
      defined = function.defun(variable_creator)
      defined()  # Create the variable.
      self.assertIsInstance(
          self.v, resource_variable_ops.ResourceVariable)
  
    def testRunMetadata(self):
  
      @def_function.function
      def f(x):
        return x * x
  
      with ops.device('cpu:0'):
        context.enable_run_metadata()
        f(constant_op.constant(1.0))
      run_metadata = context.export_run_metadata()
      context.disable_run_metadata()
      self.assertLen(run_metadata.partition_graphs, 1)
  
    def testGraphModeCaptureVariable(self):
      with context.graph_mode(), self.cached_session():
  
        class HasAVar(object):
  
          def __init__(self):
            self.v = resource_variable_ops.ResourceVariable(1.0)
  
          def call(self):
            return self.v * 2
  
        o = HasAVar()
        self.evaluate(variables.global_variables_initializer())
        call = def_function.function(o.call)
        op = call()
        self.assertAllEqual(self.evaluate(op), 2.0)
  
    def testGraphModeManyFunctions(self):
      with ops.Graph().as_default(), self.cached_session():
  
        @def_function.function
        def f(x):
          return x * x
  
        @def_function.function
        def g(x):
          return f(x) + 1
  
        self.assertAllEqual(g(constant_op.constant(2.0)), 5.0)
  
    def testDict(self):
  
      @def_function.function
      def f(x):
        return {'name': x + 1}
  
      self.assertAllEqual(f(constant_op.constant(1.0))['name'], 2.0)
  
    def testWeakrefInputsRejected(self):
  
      @def_function.function
      def f(x):
        return x
  
      class Dummy:
        pass
      o = Dummy()
      wr = weakref.ref(o)
  
      with self.assertRaisesRegex(ValueError, 'weakref'):
        f(wr)
  
    def testTensorConversionWithDefun(self):
  
      @def_function.function
      def f(x):
        return math_ops.add(x, constant_op.constant(3))
  
      self.assertAllEqual(5, f(constant_op.constant(2)))
  
    def testTensorConversionCall(self):
  
      @def_function.function
      def f(x):
        return math_ops.add(x, constant_op.constant(3))
  
      @def_function.function
      def g(x):
        return f(f(x))
  
      self.assertAllEqual(8, g(constant_op.constant(2)))
  
    def testCallShape(self):
  
      @def_function.function
      def f(x):
        return x + 1
  
      @def_function.function
      def g(x):
        x = f(x)
        self.assertEqual(x.shape.as_list(), [])
        return None
  
      g(constant_op.constant(1.0))
  
    def testNestedDefunWithNoOutputAndTapedInput(self):
      three = resource_variable_ops.ResourceVariable(3.0, name='v')
  
      @def_function.function
      def f(x):
        # This function intentionally takes a taped variable as input,
        # but does not return any values
        math_ops.add(x, three)
  
      @def_function.function
      def g(x):
        y = math_ops.add(x, three)
        f(y)
  
      g(three)
  
    def testGatherResourceWithDefun(self):
      with ops.device('cpu:0'):
        v = resource_variable_ops.ResourceVariable([0.0, 1.0, 2.0])
  
      def sum_gather():
        return math_ops.reduce_sum(array_ops.gather(v, [1, 2]))
  
      defined = def_function.function(sum_gather)
      self.assertAllEqual(sum_gather(), defined())
  
    @parameterized.named_parameters([
        ('IndexedSlicesWithDenseShape',
         _example_indexed_slices_with_dense_shape,),
        ('IndexedSlicesWithoutDenseShape',
         _example_indexed_slices_without_dense_shape,),
        ('RaggedTensorRaggedRank1', ragged_tensor.RaggedTensor.from_row_lengths,
         {'values': [1, 2, 3], 'row_lengths': [2, 0, 1]}),
        ('RaggedTensorRaggedRank2',
         ragged_tensor.RaggedTensor.from_nested_row_lengths,
         {'flat_values': [1, 2, 3], 'nested_row_lengths': [[1, 2], [2, 0, 1]]}),
        ('SparseTensor', sparse_tensor.SparseTensor,
         {'values': [1, 2, 3], 'indices': [[0], [8], [10]], 'dense_shape': [20]}),
    ])  # pyformat: disable
    def testReturnCompositeTensorWithDefun(self,
                                           factory_fn,
                                           factory_kwargs={},
                                           input_signature=None):
      input_ct = factory_fn(**factory_kwargs)
  
      @def_function.function(input_signature=input_signature)
      def f():
        return input_ct
  
      output_ct = f()
      self.assertIsInstance(output_ct, type(input_ct))
      nest.assert_same_structure(input_ct, output_ct, expand_composites=True)
  
      input_flat = nest.flatten(input_ct, expand_composites=True)
      output_flat = nest.flatten(output_ct, expand_composites=True)
      for (input_component, output_component) in zip(input_flat, output_flat):
        self.assertAllEqual(input_component, output_component)
  
    @parameterized.named_parameters([
        ('IndexedSlicesWithDenseShape',
         _example_indexed_slices_with_dense_shape,),
        ('IndexedSlicesWithoutDenseShape',
         _example_indexed_slices_without_dense_shape,),
        ('RaggedTensorRaggedRank1',
         ragged_tensor.RaggedTensor.from_row_lengths,
         {'values': [1, 2, 3], 'row_lengths': [2, 0, 1]}),
        ('RaggedTensorRaggedRank2',
         ragged_tensor.RaggedTensor.from_nested_row_lengths,
         {'flat_values': [1, 2, 3], 'nested_row_lengths': [[1, 2], [2, 0, 1]]}),
        ('SparseTensor',
         sparse_tensor.SparseTensor,
         {'values': [1, 2, 3], 'indices': [[0], [8], [10]], 'dense_shape': [20]}),
        ('RaggedTensorRaggedRank1WithSignature',
         ragged_tensor.RaggedTensor.from_row_lengths,
         {'values': [1, 2, 3], 'row_lengths': [2, 0, 1]},
         [ragged_tensor.RaggedTensorSpec([None, None], dtypes.int32)]),
        ('RaggedTensorRaggedRank2WithSignature',
         ragged_tensor.RaggedTensor.from_nested_row_lengths,
         {'flat_values': [1, 2, 3], 'nested_row_lengths': [[1, 2], [2, 0, 1]]},
         [ragged_tensor.RaggedTensorSpec([None, None, None], dtypes.int32)]),
        ('SparseTensorWithSignature',
         sparse_tensor.SparseTensor,
         {'values': [1, 2, 3], 'indices': [[0], [8], [10]], 'dense_shape': [20]},
         [sparse_tensor.SparseTensorSpec([None], dtypes.int32)]),
    ])  # pyformat: disable
    def testCompositeAsArgumentTensorWithDefun(self,
                                               factory_fn,
                                               factory_kwargs={},
                                               input_signature=None):
      input_ct = factory_fn(**factory_kwargs)
  
      @def_function.function(input_signature=input_signature)
      def f(x):
        return x
  
      output_ct = f(input_ct)
      self.assertIsInstance(output_ct, type(input_ct))
      nest.assert_same_structure(input_ct, output_ct, expand_composites=True)
  
      input_flat = nest.flatten(input_ct, expand_composites=True)
      output_flat = nest.flatten(output_ct, expand_composites=True)
      for (input_component, output_component) in zip(input_flat, output_flat):
        self.assertAllEqual(input_component, output_component)
  
    def testTracedCompositeDiscardsShapeInfo(self):
      # SparseTensorSpec intentionally excludes info about the number of elements
      # that are in a sparse tensor (which is recorded as st.indices.shape[0] and
      # st.values.shape[0]).  Similarly, RaggedTensorSpec intentionally excludes
      # info about the total number of values in a RaggedTensor (stored as
      # rt.values.shape[0]).  This test checks that the placeholders created by
      # tf.function() properly mask this shape info.
      @def_function.function
      def f(rt, st):
        self.assertEqual(st.indices.shape.as_list()[:1], [None])
        self.assertEqual(st.values.shape.as_list(), [None])
        return (rt, st)
  
      rt = ragged_factory_ops.constant([[1, 2], [3]])
      st = sparse_tensor.SparseTensor([[0]], [0], [10])
      f(rt, st)
  
    @test_util.run_gpu_only
    def testFunctionOnDevice(self):
      x = constant_op.constant([1.]).gpu()
      f = def_function.function(math_ops.add)
      y = f(x, x).cpu()
      self.assertAllEqual(y, [2.])
  
    @test_util.run_gpu_only
    @test_util.run_in_graph_and_eager_modes
    def testFunctionWithResourcesOnDifferentDevices(self):
      with ops.device('/cpu:0'):
        v_cpu = resource_variable_ops.ResourceVariable([0.0, 1.0, 2.0])
  
      with ops.device('/gpu:0'):
        v_gpu = resource_variable_ops.ResourceVariable([0.0, 1.0, 2.0])
  
      def sum_gather():
        cpu_result = math_ops.reduce_sum(array_ops.gather(v_cpu, [1, 2]))
        gpu_result = math_ops.reduce_sum(array_ops.gather(v_gpu, [1, 2]))
        return cpu_result, gpu_result
  
      defined = function.defun(sum_gather)
      if not context.executing_eagerly():
        self.evaluate(variables.global_variables_initializer())
      expected = self.evaluate(sum_gather())
      self.assertAllEqual(expected, self.evaluate(defined()))
  
    @test_util.run_gpu_only
    @test_util.run_in_graph_and_eager_modes
    def testOpInFunctionWithConflictingResourceInputs(self):
      with ops.device('/cpu:0'):
        v_cpu = resource_variable_ops.ResourceVariable(
            [0.0, 1.0, 2.0], name='cpu')
        v_also_cpu = resource_variable_ops.ResourceVariable(
            [0.0, 1.0, 2.0], name='also_cpu')
  
      with ops.device('/gpu:0'):
        v_gpu = resource_variable_ops.ResourceVariable(
            [0.0, 1.0, 2.0], name='gpu')
  
      @def_function.function
      def resource_apply_adam():
        training_ops.resource_apply_adam(
            v_cpu.handle,
            v_gpu.handle,
            v_also_cpu.handle,
            1.0,  # beta1_power
            1.0,  # beta2_power
            1.0,  # learning_rate
            1.0,  # beta1
            1.0,  # beta2
            1.0,  # epsilon,
            [1.0, 1.0, 1.0],  # grad
            False)  # use_locking
        return None
  
      with self.assertRaisesRegex(
          errors.InvalidArgumentError,
          'Cannot place the graph because a reference or resource edge connects '
          'colocation groups with incompatible assigned devices'):
        if not context.executing_eagerly():
          self.evaluate(variables.global_variables_initializer())
        self.evaluate(resource_apply_adam())
  
    @test_util.run_gpu_only
    def testFunctionHandlesInputsOnDifferentDevices(self):
      # The Reshape op requires the shape tensor to be placed in host memory.
      reshape = def_function.function(array_ops.reshape)
      value = constant_op.constant([1., 2.]).gpu()
      shape = constant_op.constant([2, 1])
      reshaped = reshape(value, shape).cpu()
      self.assertAllEqual(reshaped, [[1], [2]])
  
    @test_util.run_gpu_only
    def testFunctionHandlesInputsPlacedOnTheWrongDeviceGracefully(self):
      # The Reshape op requires the shape tensor to be placed in host memory.
      reshape = def_function.function(array_ops.reshape)
      value = constant_op.constant([1., 2.])
      shape = constant_op.constant([2, 1]).gpu()
      reshape(value, shape)  # No error is raised
  
    def testNoneOutput(self):
  
      @def_function.function
      def my_function(_):
        return None
  
      self.assertAllEqual(my_function(1), None)
  
    def testNestedFunctions(self):
      # TensorFlow function (which is what would be used in TensorFlow graph
      # construction).
      @tf_function.Defun(dtypes.int32, dtypes.int32)
      def add(a, b):
        return math_ops.add(a, b)
  
      @def_function.function
      def add_one(x):
        return add(x, 1)
  
      self.assertAllEqual(3, add_one(constant_op.constant(2)))
  
    def testVariableCaptureInNestedFunctions(self):
      v = resource_variable_ops.ResourceVariable(1, dtype=dtypes.int32)
  
      @def_function.function
      def inner_read():
        return v.read_value()
  
      @def_function.function
      def outer():
        return inner_read()
  
      self.assertEqual(1, int(outer()))
  
    def testReturnCapturedEagerTensor(self):
      t = constant_op.constant(1)
  
      @def_function.function
      def read():
        return t
  
      self.assertEqual(1, int(read()))
  
    def testReturnCapturedGraphTensor(self):
      with context.graph_mode(), self.cached_session():
        t = constant_op.constant(1)
  
        @def_function.function
        def read():
          return t
  
        self.assertEqual(1, int(self.evaluate(read())))
  
    def testSequenceInputs(self):
      clip_by_global_norm = def_function.function(clip_ops.clip_by_global_norm)
      t_list = [constant_op.constant(1.0), constant_op.constant(2.0)]
      clipped_list, global_norm = clip_by_global_norm(t_list,
                                                      constant_op.constant(.2))
      for t in clipped_list:
        self.assertIsInstance(t, ops.Tensor)
      self.assertIsInstance(global_norm, ops.Tensor)
  
    def testNestedSequenceInputs(self):
  
      def my_op(inputs):
        a, b, c = inputs
        e, f = b
        g, h = e
        return [a + a, [tuple([f + f, g + g]), h + h], c + c], a + f + g + h + c
  
      my_eager_op = def_function.function(my_op)
      ret = my_eager_op([
          constant_op.constant(1), [(constant_op.constant(2),
                                     constant_op.constant(3)),
                                    constant_op.constant(4)],
          constant_op.constant(5)
      ])
      self.assertLen(ret, 2)
      self.assertAllEqual(ret[0][0], 2)
      self.assertAllEqual(ret[0][1][0][0], 8)
      self.assertAllEqual(ret[0][1][0][1], 4)
      self.assertIsInstance(ret[0][1][0], tuple)
      self.assertAllEqual(ret[0][1][1], 6)
      self.assertAllEqual(ret[0][2], 10)
      self.assertAllEqual(ret[1], 15)
  
    def testVariableNamesRespectNameScopesWithDefun(self):
      @def_function.function
      def create_variable():
        with ops.name_scope('foo', skip_on_eager=False):
          v = resource_variable_ops.ResourceVariable(0.0, name='bar')
        self.assertEqual(v.name, 'foo/bar:0')
  
      create_variable()
  
    def testVariableNamesRespectNameScopesWithDefunInGraph(self):
      with context.graph_mode():
        @def_function.function
        def create_variable():
          with ops.name_scope('foo', skip_on_eager=False):
            v = resource_variable_ops.ResourceVariable([1.0, 2.0], name='bar')
          self.assertEqual(v.name, 'foo/bar:0')
  
        with ops.get_default_graph().as_default():
          create_variable()
  
    @test_util.assert_no_new_pyobjects_executing_eagerly
    def testCallOptionsMemory(self):
  
      @function.defun
      def model(x):
        return x + constant_op.constant(1.)
  
      # This happens with a lot of option toggles, e.g. soft device placement
      context.context().function_call_options = None
      model(constant_op.constant(2.))
  
    @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
    def testLayerInDefun(self):
      conv = convolutional.Conv2D(
          filters=1,
          kernel_size=2,
          kernel_initializer=init_ops.ones_initializer(),
          bias_initializer=init_ops.zeros_initializer())
  
      @function.defun
      def model(x):
        return conv(x)
  
      x = array_ops.ones([1, 2, 2, 1])
      y = model(x)
  
      if not context.executing_eagerly():
        self.evaluate(variables.global_variables_initializer())
  
      self.assertAllClose([[[[4.0]]]], self.evaluate(y))
  
    # Variable lifting is somewhat different between defun/tf.function, so testing
    # device placement on both makes sense.
    @parameterized.named_parameters(
        dict(testcase_name='Defun',
             function_decorator=function.defun),
        dict(testcase_name='DefFunction',
             function_decorator=def_function.function))
    @test_util.run_in_graph_and_eager_modes
    def testVariablesPlacedOnOutsideDevice(self, function_decorator):
  
      class _Obj(object):
  
        def __init__(self):
          self.v = None
  
        @function_decorator
        def f(self):
          if self.v is None:
            self.v = variables.Variable(1.)
          return self.v + 1.
  
      has_device = _Obj()
      with ops.device('cpu:0'):
        has_device.f()
      self.assertIn('CPU', has_device.v.device)
  
    @test_util.run_in_graph_and_eager_modes
    def testMultipleDeviceCheck(self):
  
      def f():
        with ops.device('cpu'):
          return test_ops.device_placement_op()
  
      func = function.defun(f)
      with ops.device('cpu:0'):
        output = self.evaluate(func())
        self.assertIn(compat.as_bytes('CPU:0'), output)
  
    @test_util.run_in_graph_and_eager_modes
    def testDeviceAnnotationsRespected(self):
  
      def multi_device_fn():
        with ops.device('/cpu:0'):
          s0 = test_ops.device_placement_op()
        with ops.device('/cpu:1'):
          s1 = test_ops.device_placement_op()
        with ops.device('/cpu:2'):
          s2 = test_ops.device_placement_op()
        s3 = test_ops.device_placement_op()
        return s0, s1, s2, s3
  
      defined = function.defun(multi_device_fn)
      outputs = self.evaluate(defined())
      self.assertLen(total_function_cache(defined), 1)
      self.assertIn(compat.as_bytes('CPU:0'), outputs[0])
      self.assertIn(compat.as_bytes('CPU:1'), outputs[1])
      self.assertIn(compat.as_bytes('CPU:2'), outputs[2])
  
      with ops.device('/cpu:3'):
        outputs = self.evaluate(defined())
      # All function definitions are agnostic to call site devices.
      self.assertLen(total_function_cache(defined), 1)
      self.assertIn(compat.as_bytes('CPU:0'), outputs[0])
      self.assertIn(compat.as_bytes('CPU:1'), outputs[1])
      self.assertIn(compat.as_bytes('CPU:2'), outputs[2])
      self.assertIn(compat.as_bytes('CPU:3'), outputs[3])
  
      with ops.device('/cpu:0'):
        outputs = self.evaluate(defined())
      self.assertLen(total_function_cache(defined), 1)
      self.assertIn(compat.as_bytes('CPU:0'), outputs[0])
      self.assertIn(compat.as_bytes('CPU:1'), outputs[1])
      self.assertIn(compat.as_bytes('CPU:2'), outputs[2])
      self.assertIn(compat.as_bytes('CPU:0'), outputs[3])
  
    @test_util.run_in_graph_and_eager_modes
    def testCallingGraphFunctionOnDifferentDevice(self):
  
      def func():
        return constant_op.constant(0)
  
      defined = def_function.function(func)
      with ops.device('cpu:0'):
        cpu_graph_function = defined.get_concrete_function()
  
      with ops.device('cpu:0'):
        self.assertEqual(
            self.evaluate(cpu_graph_function()), self.evaluate(func()))
  
      with ops.device('cpu:1'):
        self.assertEqual(0., self.evaluate(cpu_graph_function()))
  
      with ops.device(None):
        self.assertEqual(0., self.evaluate(cpu_graph_function()))
  
      default_graph_function = defined.get_concrete_function()
      self.assertEqual(
          self.evaluate(default_graph_function()), self.evaluate(func()))
  
      with ops.device('cpu:1'):
        self.assertEqual(0., self.evaluate(default_graph_function()))
  
    @test_util.run_gpu_only
    @test_util.run_in_graph_and_eager_modes
    def testColocateWithRespected(self):
      # TODO(b/113291792): Use multiple CPUs instead of a GPU.
      with ops.device('cpu:0'):
        x = array_ops.identity(1.0)
  
      with ops.device('gpu:0'):
        y = array_ops.identity(1.0)
  
      @def_function.function
      def foo():
        return test_ops.device_placement_op()
  
      with ops.colocate_with(x):
        self.assertIn(compat.as_bytes('CPU:0'), self.evaluate(foo()))
  
      with ops.colocate_with(y):
        self.assertIn(compat.as_bytes('GPU:0'), self.evaluate(foo()))
  
    def testVariablesAreTracked(self):
      v = resource_variable_ops.ResourceVariable(1.0)
  
      def foo(x):
        return v * x
  
      defined = def_function.function(foo)
  
      x = constant_op.constant([1.0])
      self.assertEqual(1., self.evaluate(defined(x)))
      v.assign(2.)
  
      x = constant_op.constant([1.0, 2.0])
      self.assertAllEqual([2., 4.], self.evaluate(defined(x)))
  
    def testCacheObjectHashCollisions(self):
  
      class Foo(object):
  
        def __hash__(self):
          return 42
  
      def func(foo):
        del foo
        return
  
      defined = function.defun(func)
      defined(Foo())
      self.assertLen(total_function_cache(defined), 1)
  
      defined(Foo())
      self.assertLen(total_function_cache(defined), 2)
  
    def testCacheTensorDtypeCollision(self):
  
      def func(t):
        return t + t
  
      defined = function.defun(func)
      t = constant_op.constant([[1.0]], dtype=dtypes.complex64)
      defined(t)
      self.assertLen(total_function_cache(defined), 1)
  
      t = constant_op.constant([[1.0]], dtype=dtypes.complex128)
      defined(t)
      self.assertLen(total_function_cache(defined), 2)
  
    def testCacheTensorShapeCollision(self):
  
      def func(t):
        return t + t
  
      defined = function.defun(func)
      t = constant_op.constant([[1.0]], dtype=dtypes.complex64)
      defined(t)
      self.assertLen(total_function_cache(defined), 1)
  
      t = constant_op.constant([1.0], dtype=dtypes.complex64)
      defined(t)
      self.assertLen(total_function_cache(defined), 2)
  
    def testCacheTensorShapeDtypeCollision(self):
  
      def func(t):
        return t + t
  
      defined = function.defun(func)
      t = constant_op.constant([[1.0]], dtype=dtypes.complex64)
      defined(t)
      self.assertLen(total_function_cache(defined), 1)
  
      t = constant_op.constant([1.0], dtype=dtypes.complex128)
      defined(t)
      self.assertLen(total_function_cache(defined), 2)
  
    def testCacheTensorUnknownShapesCollisionRelaxedShapes(self):
  
      def func(t):
        return t + t
  
      with context.graph_mode(), self.cached_session():
        defined = function.defun(func, experimental_relax_shapes=True)
  
        p = array_ops.placeholder(dtype=dtypes.float32, shape=[])
        defined(p)
        self.assertLen(total_function_cache(defined), 1)
  
        p = array_ops.placeholder(dtype=dtypes.float32, shape=[1])
        defined(p)
        self.assertLen(total_function_cache(defined), 2)
  
        p = array_ops.placeholder(dtype=dtypes.float32, shape=[2])
        defined(p)
        # Gradual shape relaxation is performed; and the common shape between
        # [1] and [2] is one containing unknown dimensions.
        self.assertLen(total_function_cache(defined), 2)
  
        # pylint: disable=protected-access
        self.assertLen(defined._function_cache.arg_relaxed_specs, 1)
        relaxed_specs = (
            list(defined._function_cache.arg_relaxed_specs.values())[0])
        self.assertLen(relaxed_specs, 1)
        relaxed_shape = relaxed_specs[0].shape
        # pylint: enable=protected-access
        self.assertEqual(relaxed_shape.rank, 1)
        self.assertEqual(tensor_shape.dimension_value(relaxed_shape[0]), None)
  
        t = constant_op.constant([1.0, 1.0, 1.0], dtype=dtypes.float32)
        defined(t)
        # Shape (3,) matches the relaxed shape TensorShape([None])
        self.assertLen(total_function_cache(defined), 2)
  
    def testPythonFunctionWithDefaultArgs(self):
  
      def func(foo, bar=1, baz=2):
        del foo
        del bar
        del baz
        return
  
      defined = function.defun(func)
      defined(0, baz=20)
      self.assertLen(total_function_cache(defined), 1)
  
      defined(1)  # bar=1, baz=2
      self.assertLen(total_function_cache(defined), 2)
  
      # This matches the previous call.
      defined(foo=1)
      self.assertLen(total_function_cache(defined), 2)
  
      defined(1, 2, 3)
      self.assertLen(total_function_cache(defined), 3)
  
      # This matches the previous call.
      defined(1, bar=2, baz=3)
      self.assertLen(total_function_cache(defined), 3)
  
      # This matches the previous call.
      defined(1, baz=3, bar=2)
      self.assertLen(total_function_cache(defined), 3)
  
    def testDatasetIteratorCaching(self):
      def func(it1, it2):
        next(it1)
        next(it2)
        return 0
  
      defined = function.defun(func)
  
      d = dataset_ops.DatasetV2.from_tensor_slices([1, 2, 3])
      it1 = iter(d)
      it2 = iter(d)
      _ = defined(it1, it2)  # The two iterators are different
      self.assertLen(total_function_cache(defined), 1)
  
      it3 = iter(d)
      it4 = iter(d)
      _ = defined(it3, it4)  # The two iterators are different, should not retrace
      self.assertLen(total_function_cache(defined), 1)
  
      it5 = iter(d)
      _ = defined(it5, it5)  # The two iterators are the same, should retrace
      self.assertLen(total_function_cache(defined), 2)
  
    def testFunctoolsPartialUnwrappedCorrectly(self):
  
      def full_function(a, b, c=3):
        return a, b, c
  
      partial = functools.partial(full_function, 1, c=4)
      a, b, c = partial(2)
  
      defined = function.defun(partial)
      func_a, func_b, func_c = defined(2)
      self.assertEqual(func_a.numpy(), a)
      self.assertEqual(func_b.numpy(), b)
      self.assertEqual(func_c.numpy(), c)
  
    def testInputSignatureWithMatchingInputs(self):
  
      def foo(a):
        self.assertEqual(a.shape, (2,))
        return a
  
      signature = [tensor_spec.TensorSpec(shape=(2,), dtype=dtypes.float32)]
      defined = function.defun(foo, input_signature=signature)
      a = array_ops.ones([2])
      self.assertAllEqual(a, defined(a))
      self.assertLen(total_function_cache(defined), 1)
      self.assertAllEqual(a, defined.get_concrete_function()(a))
      self.assertAllEqual(a, defined.get_concrete_function(a)(a))
      self.assertAllEqual(a, defined.get_concrete_function(
          tensor_spec.TensorSpec((2,), dtype=dtypes.float32))(a))
      self.assertLen(total_function_cache(defined), 1)
  
      def bar(a):
        self.assertEqual(a._shape_tuple(), (2, None))
        return a
  
      signature = [tensor_spec.TensorSpec((2, None), dtypes.float32)]
      defined = function.defun(bar, input_signature=signature)
      a = array_ops.ones([2, 1])
      out = defined(a)
      self.assertLen(total_function_cache(defined), 1)
      self.assertAllEqual(out, a)
  
      # Changing the second dimension shouldn't create a new function.
      b = array_ops.ones([2, 3])
      out = defined(b)
      self.assertLen(total_function_cache(defined), 1)
      self.assertAllEqual(out, b)
  
    def testInputSignatureWithDictInPositionalArgs(self):
  
      @function.defun
      def f(*_args, **_kwargs):
        return None
  
      f(1, x=2)
      self.assertLen(total_function_cache(f), 1)
      f(1, x=2)
      self.assertLen(total_function_cache(f), 1)
      f(1, {'x': 2})
      self.assertLen(total_function_cache(f), 2)
  
    def testInputSignatureWithCompatibleInputs(self):
  
      rank2_spec = tensor_spec.TensorSpec(shape=(None, None),
                                          dtype=dtypes.float32)
  
      @function.defun(input_signature=[rank2_spec])
      def func(a):
        self.assertEqual([None, None], a.shape.as_list())
        return array_ops.shape(a)
  
      self.assertAllEqual([3, 1], func([[0], [1.0], [1]]))
      self.assertAllEqual([2, 2], func(numpy.array([[1, 1], [2, 2]])))
  
      with self.assertRaisesRegex(ValueError, 'incompatible'):
        func([0.0, 1.0, 2.0])  # Wrong shape.
  
      with self.assertRaisesRegex(ValueError, 'incompatible'):
        func([['wrong dtype']])
  
    def testNoKeywordOnlyArgumentsWithInputSignature(self):
      if sys.version_info[0] < 3:
        self.skipTest('keyword_only arguments only exist in Python 3.')
  
      func = eval('lambda x, *, y: x')  # pylint: disable=eval-used
      signature = [tensor_spec.TensorSpec(None, dtypes.int32)]
      with self.assertRaisesRegex(
          ValueError, 'Cannot define a TensorFlow function from a Python '
          'function with keyword-only arguments when input_signature is '
          'provided.'):
        def_function.function(func, signature)
  
    def testNestedInputSignatures(self):
  
      def expected_foo(a, b):
        return [a, b]
  
      @function.defun(input_signature=[
          [tensor_spec.TensorSpec((2, None), dtypes.float32)] * 2,
          tensor_spec.TensorSpec((1,), dtypes.float32),
      ])
      def foo(a, b):
        self.assertEqual(a[0]._shape_tuple(), (2, None))
        self.assertEqual(a[1]._shape_tuple(), (2, None))
        self.assertEqual(b._shape_tuple(), (1,))
        return [a, b]
  
      a = array_ops.ones([2, 1])
      b = array_ops.ones([1])
      expected = expected_foo([a, a], b)
      out = foo([a, a], b)
      self.assertLen(total_function_cache(foo), 1)
      nest.assert_same_structure(out, expected)
      self.assertAllEqual(out[0][0], a)
      self.assertAllEqual(out[0][1], a)
      self.assertAllEqual(out[1], b)
  
      # Changing the unspecified dimensions shouldn't create a new function.
      a = array_ops.ones([2, 3])
      b = array_ops.ones([2, 5])
      c = array_ops.ones([1])
      expected = expected_foo([a, b], c)
      out = foo([a, b], c)
      self.assertLen(total_function_cache(foo), 1)
      nest.assert_same_structure(out, expected)
      self.assertAllEqual(out[0][0], a)
      self.assertAllEqual(out[0][1], b)
      self.assertAllEqual(out[1], c)
  
      # Passing compatible inputs should work.
      a = a.numpy().tolist()
      b = b.numpy().tolist()
      c = c.numpy().tolist()
      out = foo([a, b], c)
      self.assertLen(total_function_cache(foo), 1)
      nest.assert_same_structure(out, expected)
      self.assertAllEqual(out[0][0], a)
      self.assertAllEqual(out[0][1], b)
      self.assertAllEqual(out[1], c)
  
    def testNestedInputSignaturesWithDict(self):
      def expected_bar(a):
        return a
  
      @function.defun(input_signature=[{
          'a': tensor_spec.TensorSpec((2, None), dtypes.float32),
          'b': tensor_spec.TensorSpec((2, None), dtypes.float32),
          'c': tensor_spec.TensorSpec((1,), dtypes.float32)}])
      def bar(a):
        self.assertEqual(a['a']._shape_tuple(), (2, None))
        self.assertEqual(a['b']._shape_tuple(), (2, None))
        self.assertEqual(a['c']._shape_tuple(), (1,))
        return a
  
      a = array_ops.ones([2, 3])
      b = array_ops.ones([1])
      inputs = {'a': a, 'b': a, 'c': b}
      expected = expected_bar(inputs)
      out = bar(inputs)
      nest.assert_same_structure(out, expected)
      self.assertAllEqual(out['a'], expected['a'])
      self.assertAllEqual(out['b'], expected['b'])
      self.assertAllEqual(out['c'], expected['c'])
  
      # Passing compatible inputs should work.
      a = a.numpy().tolist()
      b = b.numpy().tolist()
      inputs = {'a': a, 'b': a, 'c': b}
      out = bar(inputs)
      nest.assert_same_structure(out, expected)
      self.assertAllEqual(out['a'], expected['a'])
      self.assertAllEqual(out['b'], expected['b'])
      self.assertAllEqual(out['c'], expected['c'])
  
    def testInputSignatureMustBeSequenceOfTensorSpecs(self):
  
      def foo(a, b):
        del a
        del b
  
      # Signatures must consist exclusively of `TensorSpec` objects.
      signature = [(2, 3), tensor_spec.TensorSpec([2, 3], dtypes.float32)]
      with self.assertRaisesRegex(TypeError, 'input_signature.*nested sequence'):
        def_function.function(foo, input_signature=signature)
  
      # Signatures must be either lists or tuples on their outermost levels.
      signature = {'t1': tensor_spec.TensorSpec([], dtypes.float32)}
      with self.assertRaisesRegex(
          TypeError, 'input_signature must be either a '
          'tuple or a list.*'):
        function.defun(foo, input_signature=signature)
  
    @test_util.run_in_graph_and_eager_modes
    def testInputsIncompatibleWithSignatureRaisesError(self):
  
      def foo(a):
        return a
  
      signature = [tensor_spec.TensorSpec(shape=(2,), dtype=dtypes.float32)]
      defined = def_function.function(foo, input_signature=signature)
  
      # Invalid shapes.
      with self.assertRaisesRegex(ValueError, 'Python inputs incompatible.*'):
        defined(array_ops.ones([3]))
  
      with self.assertRaisesRegex(ValueError, 'Python inputs incompatible.*'):
        defined(array_ops.ones([2, 1]))
  
      # Wrong number of arguments.
      with self.assertRaisesRegex(TypeError, 'specifies 1 .* got 2'):
        defined(array_ops.ones([2]), array_ops.ones([2]))
      with self.assertRaisesRegex(ValueError,
                                  'Structure of Python function inputs.*'):
        defined()
  
      with self.assertRaisesRegex(ValueError,
                                  'inputs incompatible with input_signature'):
        defined.get_concrete_function(
            tensor_spec.TensorSpec(shape=(3,), dtype=dtypes.float32))
  
    def testMismatchedConcreteSignatureRaisesError(self):
  
      @def_function.function
      def run_test():
        @def_function.function
        def f(x):
          return x
  
        with self.assertRaisesRegex(
            TypeError, 'ConcreteFunction .* was constructed .* but was called'):
          f.get_concrete_function(1)(constant_op.constant(1))
  
        with self.assertRaisesRegex(TypeError, r'f\(x\) expected .* but got .*'):
          f.get_concrete_function(constant_op.constant(1))(1)
  
        with self.assertRaisesRegex(
            TypeError, 'ConcreteFunction .* was constructed .* but was called'):
          f.get_concrete_function(1)(2)
  
      run_test()
  
    def testInputsIncompatibleWithNestedSignatureRaisesError(self):
  
      def foo(a, b):
        return [a, b]
  
      signature = [[tensor_spec.TensorSpec((1,), dtypes.float32)] * 2,
                   [tensor_spec.TensorSpec((1,), dtypes.float32)] * 2]
      defined = function.defun(foo, input_signature=signature)
      a = array_ops.ones([1])
  
      with self.assertRaisesRegex(ValueError,
                                  'Structure of Python function inputs.*'):
        defined([a, a, a], [a])
  
      with self.assertRaisesRegex(ValueError,
                                  'Structure of Python function inputs.*'):
        defined([a], [a, a, a])
      defined([a, a], [a, a])
  
    def testUnderspecifiedInputSignature(self):
      @function.defun(input_signature=[
          tensor_spec.TensorSpec([], dtypes.float32),
      ])
      def foo(a, training=True):
        if training:
          return a
        else:
          return -1.0 * a
  
      x = constant_op.constant(1.0)
      with self.assertRaisesRegex(
          TypeError, 'got keyword argument `training` '
          'that was not included in input_signature'):
        foo(x, training=True)
  
      with self.assertRaisesRegex(
          TypeError, 'got keyword argument `training` '
          'that was not included in input_signature'):
        foo(x, training=False)
  
      self.assertAllEqual(x.numpy(), foo(x).numpy())
  
    def testInputSignatureWithPartialFunction(self):
      def full_function(a, b, c=3.0):
        return a, b, c
  
      partial = functools.partial(full_function, 1, c=4)
      a, b, c = partial(2.0)
      signature = [tensor_spec.TensorSpec([], dtypes.float32)]
      defined = function.defun(partial, input_signature=signature)
      x = constant_op.constant(2.0)
      func_a, func_b, func_c = defined(x)
      self.assertEqual(func_a.numpy(), a)
      self.assertEqual(func_b.numpy(), b)
      self.assertEqual(func_c.numpy(), c)
  
    def testInputSignatureConversionWithDefaultArg(self):
  
      def foo(a, training=True):
        if training:
          return a
        else:
          return -1.0 * a
  
      signature = [
          tensor_spec.TensorSpec([], dtypes.float32),
          tensor_spec.TensorSpec([], dtypes.bool),
      ]
      defined = def_function.function(foo, input_signature=signature)
      a = constant_op.constant(1.0)
      self.assertAllEqual(a.numpy(), defined(a))
      self.assertAllEqual(a.numpy(), defined(a, training=True))
      self.assertAllEqual(-a.numpy(), defined(a, training=False))
  
    def testInputSignatureWithKeywordPositionalArgs(self):
  
      @function.defun(input_signature=[
          tensor_spec.TensorSpec([], dtypes.float32),
          tensor_spec.TensorSpec([], dtypes.int64)
      ])
      def foo(flt, integer):
        return flt, integer
  
      flt = constant_op.constant(1.0)
      integer = constant_op.constant(2, dtypes.int64)
  
      out1, out2 = foo(flt, integer)
      self.assertLen(total_function_cache(foo), 1)
      self.assertEqual(out1.numpy(), 1.0)
      self.assertEqual(out2.numpy(), 2)
  
      out1, out2 = foo(flt=flt, integer=integer)
      self.assertLen(total_function_cache(foo), 1)
      self.assertEqual(out1.numpy(), 1.0)
      self.assertEqual(out2.numpy(), 2)
  
      out1, out2 = foo(integer=integer, flt=flt)
      self.assertLen(total_function_cache(foo), 1)
      self.assertEqual(out1.numpy(), 1.0)
      self.assertEqual(out2.numpy(), 2)
  
      out1, out2 = foo(flt, integer=integer)
      self.assertLen(total_function_cache(foo), 1)
      self.assertEqual(out1.numpy(), 1.0)
      self.assertEqual(out2.numpy(), 2)
  
    def testInputSignatureWithKeywordArgs(self):
      def foo(a, b, **kwargs):
        del kwargs
        return a, b
  
      x = function.defun(
          foo,
          input_signature=[
              tensor_spec.TensorSpec([], dtypes.float32),
              tensor_spec.TensorSpec([], dtypes.int32)
          ]).get_concrete_function()
      result = x(constant_op.constant(5.0), constant_op.constant(5))
      self.assertAllEqual(result, [5.0, 5])
  
    def testInputSignatureWithCompositeTensors(self):
      def f(rt):
        self.assertEqual(rt.values.shape.as_list(), [None])
        self.assertEqual(rt.row_splits.shape.as_list(), [4])
        return rt
  
      signature = [ragged_tensor.RaggedTensorSpec(
          shape=[3, None], dtype=dtypes.int32)]
      defined = function.defun(f, input_signature=signature)
      rt1 = ragged_factory_ops.constant([[1], [], [2, 3, 4]])
      out1 = defined(rt1)
      self.assertLen(total_function_cache(defined), 1)
      self.assertAllEqual(out1.values, rt1.values)
      self.assertAllEqual(out1.row_splits, rt1.row_splits)
  
      # Changing the row lengths shouldn't create a new function.
      rt2 = ragged_factory_ops.constant([[1, 2], [3, 4], [5]])
      out2 = defined(rt2)
      self.assertLen(total_function_cache(defined), 1)
      self.assertAllEqual(out2.values, rt2.values)
      self.assertAllEqual(out2.row_splits, rt2.row_splits)
  
      # Different number of rows
      rt3 = ragged_factory_ops.constant([[1, 2], [3, 4], [5], [6]])
      with self.assertRaisesRegex(ValueError, 'incompatible'):
        defined(rt3)
  
      # Different dtype
      rt4 = ragged_factory_ops.constant([[1.0, 2.0], [], [3.0]])
      with self.assertRaisesRegex(ValueError, 'Structure .* does not match'):
        defined(rt4)
  
      # Different rank
      rt5 = ragged_factory_ops.constant([[[1]], [[2]], [[3]]])
      with self.assertRaisesRegex(ValueError, 'does not match'):
        defined(rt5)
  
    def testInputSignatureWithVariableArgs(self):
  
      def f(v):
        v.assign_add(1)
  
      signature = [
          resource_variable_ops.VariableSpec(shape=[], dtype=dtypes.int32)
      ]
      defined = function.defun(f, input_signature=signature)
  
      v1 = variables.Variable(0)
      v2 = variables.Variable(0)
  
      defined(v1)
      self.assertEqual(v1.numpy(), 1)
      self.assertEqual(v2.numpy(), 0)
  
      defined(v=v2)
      self.assertEqual(v1.numpy(), 1)
      self.assertEqual(v2.numpy(), 1)
  
    def testTensorKeywordArguments(self):
  
      def foo(a, b):
        del a
        return b
  
      defined = function.defun(foo)
      a = constant_op.constant(2.0)
      b = constant_op.constant([1.0, 2.0])
      one = defined(a, b)
      self.assertLen(total_function_cache(defined), 1)
  
      two = defined(a=a, b=b)
      self.assertLen(total_function_cache(defined), 1)
  
      three = defined(b=b, a=a)
      self.assertLen(total_function_cache(defined), 1)
  
      four = defined(a, b=b)
      self.assertLen(total_function_cache(defined), 1)
  
      # The next call corresponds to a new input signature, hence
      # we expect another function to be defined.
      five = defined(b, a)
      self.assertLen(total_function_cache(defined), 2)
  
      six = defined(a=b, b=a)
      self.assertLen(total_function_cache(defined), 2)
  
      seven = defined(b=a, a=b)
      self.assertLen(total_function_cache(defined), 2)
  
      self.assertAllEqual(one, [1.0, 2.0])
      self.assertAllEqual(two, [1.0, 2.0])
      self.assertAllEqual(three, [1.0, 2.0])
      self.assertAllEqual(four, [1.0, 2.0])
      self.assertAllEqual(five, 2.0)
      self.assertAllEqual(six, 2.0)
      self.assertAllEqual(seven, 2.0)
  
    def testDefuningInstanceMethod(self):
  
      integer = constant_op.constant(2, dtypes.int64)
  
      class Foo(object):
  
        def one(self, tensor):
          return tensor
  
        @def_function.function
        def two(self, tensor, other=integer):
          return self.one(tensor), other
  
      foo = Foo()
      t = constant_op.constant(1.0)
      one, two = foo.two(t)
      self.assertEqual(one.numpy(), 1.0)
      self.assertEqual(two.numpy(), 2)
  
    def testDefuningInstanceMethodWithDefaultArgument(self):
  
      integer = constant_op.constant(2, dtypes.int64)
  
      class Foo(object):
  
        @def_function.function
        def func(self, other=integer):
          return other
  
      foo = Foo()
      self.assertEqual(foo.func().numpy(), int(integer))
  
    def testPythonCallWithSideEffects(self):
      state = []
  
      @def_function.function
      def side_effecting_function():
        state.append(0)
  
      side_effecting_function()
      self.assertAllEqual(state, [0])
  
      # The second invocation should call the graph function, which shouldn't
      # trigger the list append.
      side_effecting_function()
      self.assertAllEqual(state, [0])
  
      # Whereas calling the python function directly should create a side-effect.
      side_effecting_function.python_function()
      self.assertAllEqual(state, [0, 0])
  
    def testFunctionWithNestedFunctionCallAndSideEffects(self):
      v1 = variables.Variable(1.0)
      v2 = variables.Variable(1.0)
  
      @def_function.function
      def add_one(a):
        a.assign_add(1.0)
  
      # Grappler will inline calls to `add_one` into the function body, we check
      # that all side-effects were executed.
      @def_function.function
      def side_effecting_function(a, b):
        add_one(a)
        add_one(b)
        return a + b
  
      result = side_effecting_function(v1, v2)
      self.assertEqual(result.numpy(), 4.0)
  
    def testFunctionWithExtraAttributes(self):
      @function.defun_with_attributes(attributes={'experimental_1': 'value1',
                                                  'experimental_2': 2})
      def matmul(x, y):
        return math_ops.matmul(x, y)
  
      def add(x, y):
        return math_ops.add(x, y)
      defun_add = function.defun_with_attributes(
          add, attributes={'experimental_3': True, 'experimental_4': 1.0})
  
      with context.graph_mode(), self.cached_session():
        with ops.get_default_graph().as_default():
          t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
          sq = matmul(t, t)
          double = defun_add(t, t)
          self.assertAllEqual(sq.eval().reshape(-1), [7, 10, 15, 22])
          self.assertAllEqual(double.eval().reshape(-1), [2, 4, 6, 8])
  
          graph = ops.get_default_graph()
          # pylint: disable=protected-access
          self.assertLen(graph._functions, 2)
          functions = list(graph._functions.values())
          self.assertRegex(functions[0].definition.signature.name, '.*matmul.*')
          attrs = functions[0].definition.attr
          self.assertLen(attrs, 2)
          self.assertEqual(attrs['experimental_1'].s, b'value1')
          self.assertEqual(attrs['experimental_2'].i, 2)
  
          self.assertRegex(functions[1].definition.signature.name, '.*add.*')
          attrs = functions[1].definition.attr
          self.assertLen(attrs, 2)
          self.assertEqual(attrs['experimental_3'].b, True)
          self.assertEqual(attrs['experimental_4'].f, 1.0)
          # pylint: enable=protected-access
  
    def testFunctionWithInvalidAttribute(self):
      @function.defun_with_attributes(attributes={'experimental_1': ['value1']})
      def add(x, y):
        return math_ops.add(x, y)
  
      with self.assertRaisesRegex(ValueError,
                                  'Attribute experimental_1 must be .* Got .*'):
        with context.graph_mode(), self.cached_session():
          with ops.get_default_graph().as_default():
            t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
            add(t, t)
  
    def testRegisterFunction(self):
  
      @function.defun
      def add(x, y):
        return math_ops.add(x, y)
  
      def matmul(x, y):
        return math_ops.matmul(x, y)
      defun_matmul = function.defun(matmul)
  
      with context.graph_mode(), self.cached_session():
        with ops.get_default_graph().as_default():
          t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
          function.register(defun_matmul, t, t)
          function.register(add, t, t)
  
          graph = ops.get_default_graph()
          # pylint: disable=protected-access
          self.assertLen(graph._functions, 6)
          # two sets of functions, each of them are (inference, forward, backward)
          functions = list(graph._functions.values())
          captured_function_names = [
              f.definition.signature.name for f in functions
          ]
          expected_func_name_regex = [
              '.*inference.*matmul.*',
              '.*forward.*matmul.*',
              '.*inference.*backward.*matmul.*',
              '.*inference.*add.*',
              '.*forward.*add.*',
              '.*inference.*backward.*add.*',
          ]
          for i in range(len(functions)):
            self.assertRegex(captured_function_names[i],
                             expected_func_name_regex[i])
  
          # Check the forward and backward function has the correct attributes.
          self.assertEqual(
              functions[1].definition.attr['backward_function_name'].s,
              functions[2].name)
          self.assertEqual(
              functions[2].definition.attr['forward_function_name'].s,
              functions[1].name)
  
          self.assertEqual(
              functions[4].definition.attr['backward_function_name'].s,
              functions[5].name)
          self.assertEqual(
              functions[5].definition.attr['forward_function_name'].s,
              functions[4].name)
  
          sq = defun_matmul(t, t)
          double = add(t, t)
          self.assertAllEqual(sq.eval().reshape(-1), [7, 10, 15, 22])
          self.assertAllEqual(double.eval().reshape(-1), [2, 4, 6, 8])
          # Make sure the pre registered function is used, and no other function
          # is added.
          self.assertLen(graph._functions, 6)
          functions = list(graph._functions.values())
          for i in range(len(functions)):
            self.assertEqual(captured_function_names[i],
                             functions[i].definition.signature.name)
  
    @parameterized.named_parameters(
        dict(testcase_name='Defun',
             function_decorator=function.defun),
        dict(testcase_name='DefFunction',
             function_decorator=def_function.function))
    def testRegisterConcreteFunction(self, function_decorator):
      @function_decorator
      def py_add(x, y):
        return math_ops.add(x, y)
  
      py_add(array_ops.ones([]), array_ops.ones([]))
      add = py_add.get_concrete_function(
          tensor_spec.TensorSpec(None, dtypes.float32),
          tensor_spec.TensorSpec(None, dtypes.float32))
  
      @function_decorator
      def py_composite(x, y):
        return x, add(x, y)
  
      py_composite(array_ops.ones([]), array_ops.ones([]))
      composite = py_composite.get_concrete_function(
          tensor_spec.TensorSpec(None, dtypes.float32),
          tensor_spec.TensorSpec(None, dtypes.float32))
  
      with context.graph_mode(), self.cached_session():
        with ops.get_default_graph().as_default():
          t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
          composite.add_to_graph()
          composite.add_gradient_functions_to_graph()
  
          graph = ops.get_default_graph()
          # pylint: disable=protected-access
          self.assertLen(graph._functions, 6)
          # two sets of functions, each of them are (inference, forward, backward)
          functions = list(graph._functions.values())
          captured_function_names = [
              f.definition.signature.name for f in functions
          ]
          expected_func_name_regex = [
              '.*inference.*py_composite.*',
              '.*inference.*py_add.*',
              '.*forward.*py_composite.*',
              '.*forward.*py_add.*',
              '.*inference.*backward.*py_composite.*',
              '.*inference.*backward.*py_add.*',
          ]
          for expected, found in zip(
              expected_func_name_regex,
              captured_function_names):
            self.assertRegex(found, expected)
  
          composite_t, composite_double = composite(t, t)
          double = add(t, t)
          self.assertAllEqual([[2, 4], [6, 8]], self.evaluate(double))
          self.assertAllEqual([[2, 4], [6, 8]], self.evaluate(composite_double))
          self.assertAllEqual([[1, 2], [3, 4]], self.evaluate(composite_t))
          # Make sure the pre registered function is used, and no other function
          # is added.
          self.assertLen(graph._functions, 6)
  
    @parameterized.named_parameters(
        dict(testcase_name='Defun',
             function_decorator=function.defun),
        dict(testcase_name='DefFunction',
             function_decorator=def_function.function))
    def testEagerCaptures(self, function_decorator):
      with context.eager_mode():
        large_tensor = array_ops.ones(shape=(256,))
        self.assertGreater(256, func_graph._EAGER_CONST_THRESHOLD)
  
        small_tensor = array_ops.ones(shape=(4,))
        self.assertLessEqual(4, func_graph._EAGER_CONST_THRESHOLD)
  
        v = resource_variable_ops.ResourceVariable(0.0)
  
      for captured, op_type in [(large_tensor, 'Placeholder'),
                                (small_tensor, 'Const'), (v, 'Placeholder')]:
        @function_decorator
        def test_fn():
          return captured + 1  # pylint: disable=cell-var-from-loop
  
        g = test_fn.get_concrete_function().graph
        internal_captures = g.internal_captures
        self.assertLen(internal_captures, 1)
        self.assertEqual(internal_captures[0].op.type, op_type)
  
    def testRegisterFunctionWithInputSignature(self):
      def matmul(x, y):
        return math_ops.matmul(x, y)
      defun_matmul = function.defun(
          matmul,
          input_signature=[
              tensor_spec.TensorSpec(shape=(2, 2), dtype=dtypes.float32),
              tensor_spec.TensorSpec(shape=(2, 2), dtype=dtypes.float32)
          ])
      with context.graph_mode(), self.cached_session():
        with ops.get_default_graph().as_default():
          t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
          function.register(defun_matmul, t, t)
  
          graph = ops.get_default_graph()
          # pylint: disable=protected-access
          self.assertLen(graph._functions, 3)
  
          # Test register function with cache, note inputs are ignored.
          function.register(defun_matmul)
          graph = ops.get_default_graph()
          self.assertLen(graph._functions, 3)
  
    def testRegisterFunctionWithCache(self):
      def matmul(x, y):
        return math_ops.matmul(x, y)
      defun_matmul = function.defun(matmul)
  
      with context.graph_mode(), self.cached_session():
        with ops.get_default_graph().as_default():
          t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
          t2 = constant_op.constant([[2.0, 3.0], [4.0, 5.0]])
          function.register(defun_matmul, t, t)
          function.register(defun_matmul, t2, t2)
  
          graph = ops.get_default_graph()
          # Only one function is registered since the input param are in same type
          # pylint: disable=protected-access
          self.assertLen(graph._functions, 3)
  
    def testCallingFunctionWithDifferentVariables(self):
  
      @function.defun
      def foo(v):
        v.assign_add(1.0)
        return v.read_value()
  
      v = resource_variable_ops.ResourceVariable(0.0)
      graph_function = foo.get_concrete_function(v)
      self.assertLen(graph_function.inputs, 1)
      self.assertEmpty(graph_function.captured_inputs)
  
      self.assertEqual(float(graph_function(v)), 1.0)
      self.assertEqual(float(graph_function(v)), 2.0)
  
      w = resource_variable_ops.ResourceVariable(0.0)
  
      @function.defun
      def bar(v):
        del v
        return constant_op.constant(1.0)
  
      graph_function = bar.get_concrete_function(v)
      self.assertEqual(float(graph_function(v)), 1.0)
      self.assertEqual(float(graph_function(w)), 1.0)
  
    def testCallingFunctionWithNonTensorsFails(self):
  
      @function.defun
      def foo(x):
        return x
  
      graph_function = foo.get_concrete_function(constant_op.constant(1.0))
      with self.assertRaises((TypeError, ValueError)):
        graph_function('Not a Tensor.')
  
    def testSwapImplementationWithGrapplerPlugin(self):
      # Set the min_graph_nodes to -1 since the graph in this test is too small,
      # and will be ignored by grappler if don't set this.
      rewrites = rewriter_config_pb2.RewriterConfig()
      rewrites.implementation_selector = rewriter_config_pb2.RewriterConfig.ON
      rewrites.min_graph_nodes = -1
      graph_options = config_pb2.GraphOptions(
          rewrite_options=rewrites, build_cost_model=1)
      config_proto = config_pb2.ConfigProto(graph_options=graph_options)
  
      with context.graph_mode(), self.cached_session(
          config=config_proto, graph=ops.Graph(), use_gpu=True):
  
        @function.defun_with_attributes(
            attributes={
                'api_implements': 'random_boost',
                'api_preferred_device': 'CPU'
            })
        def cpu_boost(x):
          return math_ops.add(x, 2.0)
  
        @function.defun_with_attributes(
            attributes={
                'api_implements': 'random_boost',
                'api_preferred_device': 'GPU'
            })
        def gpu_boost(x):
          return math_ops.add(x, 4.0)
  
        x = constant_op.constant(1.0)
  
        function.register(cpu_boost, x)
        y = gpu_boost(x)
        y_value = self.evaluate(y)
  
        if test.is_gpu_available():
          self.assertEqual(y_value, 5.0)
        else:
          # Grappler fallback to use the CPU impl even called with GPU function.
          self.assertEqual(y_value, 3.0)
  
    @test_util.disable_tfrt('b/174712583: TFRT doesn\'t support behavior '
                            'equivalent to implementation_selector for function')
    def testSwapImplementationInEager(self):
      if not context.executing_eagerly():
        self.skipTest('eager only')
  
      # testSharedRendezvous sets the disable_meta_optimizer flag to True
      # if that subtest runs before this one, then having that set to True
      # will cause this subtest to fail. To avoid that scenario, explicitly
      # set the disable_meta_optimizer flag to false here
      context.context().set_optimizer_experimental_options({
          'min_graph_nodes': -1,
          'implementation_selector': True,
          'disable_meta_optimizer': False
      })
  
      @function.defun_with_attributes(
          attributes={'api_implements': 'foo',
                      'api_preferred_device': 'CPU'})
      def on_cpu(x):
        return x + 2
  
      @function.defun_with_attributes(
          attributes={'api_implements': 'foo',
                      'api_preferred_device': 'GPU'})
      def on_gpu(x):
        return x + 4
  
      @function.defun
      def run_on_cpu(t):
        function.register(on_cpu, t)
        with ops.device('CPU:0'):
          return on_gpu(t)
  
      # Expect to run the on_cpu branch, regardless whether gpu is available.
      self.assertEqual(run_on_cpu(constant_op.constant(1)).numpy(), 3)
  
    def testDefunFunctionSeparateGraphs(self):
      with context.graph_mode():
  
        @function.defun
        def add(x):
          return x + 5
  
        @function.defun
        def maybe_add(x, should_add):
          if should_add:
            return add(x)
          else:
            return x
  
        with ops.Graph().as_default():
          x = constant_op.constant(11)
          maybe_add(x, True)
          self.assertLen(total_function_cache(maybe_add), 1)
          self.assertLen(total_function_cache(add), 1)
  
          maybe_add(x, False)
          self.assertLen(total_function_cache(maybe_add), 2)
          self.assertLen(total_function_cache(add), 1)
  
        with ops.Graph().as_default():
          x = constant_op.constant(11)
          maybe_add(x, True)
          self.assertLen(total_function_cache(maybe_add), 3)
          self.assertLen(total_function_cache(add), 2)
  
    def testCacheKeyOverlappingShapes(self):
      @function.defun
      def defined(t):
        return t
  
      defined(array_ops.zeros([12, 1]))
      self.assertLen(total_function_cache(defined), 1)
      defined(array_ops.zeros([1, 21]))
      self.assertLen(total_function_cache(defined), 2)
  
      @function.defun
      def defined_again(t):
        return defined(t)
  
      defined_again.get_concrete_function(array_ops.zeros([12, 1]))
      self.assertLen(total_function_cache(defined_again), 1)
      defined_again.get_concrete_function(array_ops.zeros([1, 21]))
      self.assertLen(total_function_cache(defined_again), 2)
  
    def testCacheTensorSpecIdenticalToTensor(self):
      @function.defun
      def defined(t):
        return t
  
      z = array_ops.zeros([2, 2])
      z_spec = tensor_spec.TensorSpec.from_tensor(z)
      self.assertIs(
          defined.get_concrete_function(z_spec), defined.get_concrete_function(z))
  
    def testCacheKeyNestedLists(self):
      @function.defun
      def defined(l):
        return l
  
      a = constant_op.constant(1.)
      b = constant_op.constant(2.)
      c = constant_op.constant(3.)
      defined([[a], b, c])
      self.assertLen(total_function_cache(defined), 1)
  
      defined([[a, b], c])
      self.assertLen(total_function_cache(defined), 2)
  
    def testCacheKeyAttrsClass(self):
      if attr is None:
        self.skipTest('attr module is unavailable.')
  
      @attr.s
      class TestClass(object):
        a = attr.ib()
        b = attr.ib()
  
      @function.defun
      def defined(l):
        return l
  
      defined(
          TestClass(
              constant_op.constant(1.),
              [constant_op.constant(2.),
               constant_op.constant(3.)]))
      self.assertLen(total_function_cache(defined), 1)
      defined(
          TestClass(
              constant_op.constant(1.),
              [constant_op.constant(2.),
               constant_op.constant(3.)]))
      self.assertLen(total_function_cache(defined), 1)
  
      defined(
          TestClass([constant_op.constant(1.),
                     constant_op.constant(2.)], constant_op.constant(3.)))
      self.assertLen(total_function_cache(defined), 2)
  
    def testCacheKeyVariables(self):
      @function.defun
      def defined(a, b, c):
        return a + b + c
  
      x = resource_variable_ops.ResourceVariable(0.0)
      y = resource_variable_ops.ResourceVariable(0.0)
      z = resource_variable_ops.ResourceVariable(0.0)
  
      # If tensor equality is not enabled, we always get a cache miss if the
      # function is called with different variables. With equality enabled we
      # should only get a miss if the aliasing changed.
      defined(x, y, z)
      self.assertLen(total_function_cache(defined), 1)
      defined(x, y, z)
      self.assertLen(total_function_cache(defined), 1)
  
      # Re-arranging arguments causes cache miss
      defined(z, y, x)
      self.assertLen(total_function_cache(defined), 2)
      defined(z, y, x)
      self.assertLen(total_function_cache(defined), 2)
  
      # Aliasing causes cache miss
      defined(x, x, z)
      self.assertLen(total_function_cache(defined), 3)
      defined(x, x, z)
      self.assertLen(total_function_cache(defined), 3)
  
      # Re-arranging arguments causes cache miss
      defined(y, y, z)
      self.assertLen(total_function_cache(defined), 4)
      defined(y, y, z)
      self.assertLen(total_function_cache(defined), 4)
  
      # Different alias positions causes cache miss
      defined(z, y, y)
      self.assertLen(total_function_cache(defined), 5)
      defined(z, y, y)
      self.assertLen(total_function_cache(defined), 5)
  
      x_copy = copy.deepcopy(x)
  
      # Deep copy causes cache miss
      defined(x_copy, y, z)
      self.assertLen(total_function_cache(defined), 6)
      defined(x_copy, y, z)
      self.assertLen(total_function_cache(defined), 6)
  
    def testVariableRetracing(self):
      v1 = variables.Variable(1.)
      v2 = variables.Variable(1.)
      v3 = copy.deepcopy(variables.Variable(1.))
  
      var_dict = {id(v1): constant_op.constant(1),
                  id(v2): constant_op.constant(2),
                  id(v3): constant_op.constant(3)}
  
      @function.defun
      def lookup_tensor(v):
        return var_dict[id(v)]
  
      self.assertEqual(1, lookup_tensor(v1).numpy())
      self.assertEqual(2, lookup_tensor(v2).numpy())
      self.assertEqual(3, lookup_tensor(v3).numpy())
  
    def testDecoratedMethodInspect(self):
  
      class DefunnedMiniModel(object):
  
        @function.defun
        def call(self, inputs, training=True):
          pass
  
      m = DefunnedMiniModel()
      fullargspec = tf_inspect.getfullargspec(m.call)
      self.assertIn('training', fullargspec.args)
  
    def testFunctionModifiesInputList(self):
      # Tests on `list` methods that do in place modification, except `list.sort`
      # since it cannot even be "defunned" in the first place
  
      def get_list():
        return [constant_op.constant(0.), constant_op.constant(1.)]
  
      expected_msg = '.*() should not modify'
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def append(l):
          l.append(constant_op.constant(0.))
  
        append(get_list())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def extend(l):
          l.extend([constant_op.constant(0.)])
  
        extend(get_list())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def insert(l):
          l.insert(0, constant_op.constant(0.))
  
        insert(get_list())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def pop(l):
          l.pop()
  
        pop(get_list())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def reverse(l):
          l.reverse()
  
        reverse(get_list())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def remove(l):
          l.remove(l[0])
  
        remove(get_list())
  
      # `list.clear` is a method that is in Py3 but not Py2
      if sys.version.startswith('3'):
  
        with self.assertRaisesRegex(ValueError, expected_msg):
  
          @def_function.function
          def clear(l):
            l.clear()
  
          clear(get_list())
  
      # One last test for keyword arguments
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def kwdappend(**kwargs):
          l = kwargs['l']
          l.append(constant_op.constant(0.))
  
        kwdappend(l=get_list())
  
    def testFunctionModifiesInputDict(self):
  
      def get_dict():
        return {'t1': constant_op.constant(0.), 't2': constant_op.constant(1.)}
  
      expected_msg = '.* should not modify'
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def clear(m):
          m.clear()
  
        clear(get_dict())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def pop(m):
          m.pop('t1')
  
        pop(get_dict())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def popitem(m):
          m.popitem()
  
        popitem(get_dict())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def update(m):
          m.update({'t1': constant_op.constant(3.)})
  
        update(get_dict())
  
      with self.assertRaisesRegex(ValueError, expected_msg):
  
        @def_function.function
        def setdefault(m):
          m.setdefault('t3', constant_op.constant(3.))
  
        setdefault(get_dict())
  
    def testFunctionModifiesInputNest(self):
      with self.assertRaisesRegex(ValueError, 'modify.* should not modify'):
  
        @def_function.function
        def modify(n):
          n[0]['t1'].append(constant_op.constant(1.))
  
        nested_input = [{
            't1': [constant_op.constant(0.),
                   constant_op.constant(1.)],
        },
                        constant_op.constant(2.)]
  
        modify(nested_input)
  
      with self.assertRaisesRegex(ValueError,
                                  'modify_same_flat.* should not modify'):
  
        # The flat list doesn't change whereas the true structure changes
        @def_function.function
        def modify_same_flat(n):
          n[0].append(n[1].pop(0))
  
        nested_input = [[constant_op.constant(0.)],
                        [constant_op.constant(1.),
                         constant_op.constant(2.)]]
  
        modify_same_flat(nested_input)
  
    @test_util.disable_tfrt('b/173429686')
    def testExecutorType(self):
      @function.defun
      def add_five(x):
        return x + 5
  
      self.assertEqual(
          5,
          add_five(constant_op.constant(0, dtype=dtypes.int32)).numpy())
  
      with self.assertRaisesRegex(errors.NotFoundError, 'NON_EXISTENT_EXECUTOR'):
        with context.function_executor_type('NON_EXISTENT_EXECUTOR'):
          add_five(constant_op.constant(0, dtype=dtypes.int32))
  
      for executor_type in ('', 'DEFAULT', None):
        with context.function_executor_type(executor_type):
          self.assertAllEqual(
              5,
              add_five(constant_op.constant(0, dtype=dtypes.int32)).numpy())
  
    @test_util.assert_no_garbage_created
    def testReferenceCycles(self):
  
      fn = function.defun(lambda x: 2. * x)
  
      fn(constant_op.constant(4.0))
      weak_fn = weakref.ref(fn)
      del fn
      # Tests that the weak reference we made to the function is now dead, which
      # means the object has been deleted. This should be true as long as the
      # function itself is not involved in a reference cycle.
      self.assertIs(None, weak_fn())
  
    def testFunctionStackInErrorMessage(self):
      if context.executing_eagerly():
        # TODO(b/122736651): Remove this skipTest once fixed.
        self.skipTest('Error interpolation is not working when function is '
                      'invoked without PartitionedCallOp.')
  
      @def_function.function()
      def fn3(x):
        return x + 2
  
      @def_function.function()
      def fn2(x):
        check_ops.assert_equal(fn3(x), 3)
        return 2
  
      @def_function.function()
      def fn(x):
        return fn2(x)
  
      with self.assertRaises(errors.InvalidArgumentError) as cm:
        fn(2)
      e = cm.exception
      self.assertIn('fn -> fn2', e.message)
      self.assertIn('node assert_equal/Assert/Assert (defined at', e.message)
      self.assertNotIn('fn3', e.message)
  
    @test_util.run_gpu_only
    def testFunctionIsNotPinned(self):
      """Tests that functions aren't pinned to the CPU by the eager runtime."""
      seed1, seed2 = 79, 25
      shape = constant_op.constant([4, 7])
      dtype = dtypes.float32
  
      @def_function.function
      def func():
        with ops.device('GPU:0'):
          return gen_random_ops.random_standard_normal(
              shape, dtype=dtype, seed=seed1, seed2=seed2)
  
      with ops.device('GPU:0'):
        x = func()
        self.assertRegex(x.device, 'GPU')
  
    @test_util.run_in_graph_and_eager_modes
    def testShapeCaching(self):
  
      @function.defun
      def func(x):
        return array_ops.shape(x)
  
      @function.defun(
          input_signature=[tensor_spec.TensorSpec([None, None], dtypes.float32)])
      def calls_func(x):
        return func(x)
  
      self.assertAllEqual([1, 1], self.evaluate(func(array_ops.zeros([1, 1]))))
      self.assertAllEqual([2, 2], self.evaluate(func(array_ops.zeros([2, 2]))))
      self.assertAllEqual(
          [3, 3],
          self.evaluate(calls_func(array_ops.zeros([3, 3]))))
  
    def testLimitedRetracing(self):
      trace_count = [0]
      @function.defun
      def func(x):
        trace_count[0] += 1
        return x
  
      for _ in range(50):
        func(constant_op.constant(3.))
        func(constant_op.constant(4.))
        func(constant_op.constant([[1., 2.]]))
        func(constant_op.constant([[]]))
        func(constant_op.constant([[3., 4.], [5., 6.]]))
        func(constant_op.constant([[3., 4.], [5., 6.], [7., 8.]]))
      # Tracing more than twice per input doesn't make sense.
      self.assertLess(trace_count[0], 13)
  
    def testLimitedRetracingWithCompositeTensors(self):
      trace_count = [0]
  
      @def_function.function
      def f(x):
        trace_count[0] += 1
        return x
  
      for i in range(10):
        f(ragged_factory_ops.constant([[1, 2], [i]]))
        f(ragged_factory_ops.constant([[1, 2], [], [3, 4, 5]]))
        f(ragged_factory_ops.constant([[[1, 2], [3]], [[4, 5, 6]]]))
        self.assertEqual(trace_count[0], 3)
  
    def test_concrete_function_shape_mismatch(self):
  
      @def_function.function
      def f(argument_name):
        return argument_name + 1.
  
      f_concrete = f.get_concrete_function(constant_op.constant([1.]))
  
      # Calling a function from eager doesn't do any shape checking above what
      # kernels do while executing.
      self.assertAllEqual(
          [2., 3.],
          f_concrete(constant_op.constant([1., 2.])).numpy())
  
      @def_function.function
      def g():
        f_concrete(constant_op.constant([1., 2.]))
  
      with self.assertRaisesRegex(ValueError, 'argument_name'):
        g()
  
    @test_util.run_in_graph_and_eager_modes
    def test_shape_inference_with_symbolic_shapes(self):
  
      @def_function.function
      def _uses_symbolic_shapes(w, x, y):
        x = array_ops.identity(x, name='name_collision')
        x = array_ops.transpose(x, [1, 0, 2])
        x_batch = array_ops.shape(x)[0]
        y_batch = array_ops.shape(y)[0]
        y *= w
        n = y_batch // x_batch
        return array_ops.reshape(y, [n, x_batch, -1])
  
      conc = _uses_symbolic_shapes.get_concrete_function(
          tensor_spec.TensorSpec(None, dtypes.float32),
          tensor_spec.TensorSpec(None, dtypes.float32),
          tensor_spec.TensorSpec(None, dtypes.float32))
  
      @def_function.function
      def _call_concrete():
        c = constant_op.constant(1.)
        array_ops.identity(c, name='name_collision')
        output1 = conc(array_ops.ones([2]),
                       array_ops.ones([5, 4, 2]),
                       array_ops.ones([20, 2]))
        self.assertEqual([5, 4, 2], output1.shape)
        output2 = conc(array_ops.ones([3]),
                       array_ops.ones([5, 4, 3]),
                       array_ops.ones([40, 3]))
        self.assertEqual([10, 4, 3], output2.shape)
        return output1, output2
  
      output1, output2 = _call_concrete()
      self.assertEqual((5, 4, 2), self.evaluate(output1).shape)
      self.assertEqual((10, 4, 3), self.evaluate(output2).shape)
  
    def testAutoGraphContext(self):
  
      @def_function.function
      def test_fn():
        self.assertEqual(
            ag_ctx.control_status_ctx().status, ag_ctx.Status.ENABLED)
  
      prev_status = ag_ctx.control_status_ctx().status
      test_fn()
      self.assertEqual(ag_ctx.control_status_ctx().status, prev_status)
  
    @test_util.disable_tfrt('b/170435618')
    def testCancelBeforeFunctionExecution(self):
      if not context.executing_eagerly():
        self.skipTest('eager only')
  
      q = data_flow_ops.FIFOQueue(1, dtypes.int32)
  
      @def_function.function
      def f():
        return q.dequeue()
  
      c_mgr = cancellation.CancellationManager()
      cancelable_func = c_mgr.get_cancelable_function(f.get_concrete_function())
  
      c_mgr.start_cancel()
      with self.assertRaises(errors.CancelledError):
        cancelable_func()
  
    @test_util.disable_tfrt('b/170435618')
    def testCancelBlockedFunctionExecution(self):
      if not context.executing_eagerly():
        self.skipTest('eager only')
  
      q = data_flow_ops.FIFOQueue(1, dtypes.int32)
  
      @def_function.function
      def f():
        return q.dequeue()
  
      c_mgr = cancellation.CancellationManager()
      cancelable_func = c_mgr.get_cancelable_function(f.get_concrete_function())
  
      def cancel_thread():
        time.sleep(0.5)
        c_mgr.start_cancel()
  
      t = self.checkedThread(cancel_thread)
      t.start()
      with self.assertRaises(errors.CancelledError):
        cancelable_func()
      t.join()
  
    @test_util.disable_tfrt('b/170435618')
    def testCancelAfterFunctionExecution(self):
      if not context.executing_eagerly():
        self.skipTest('eager only')
  
      q = data_flow_ops.FIFOQueue(1, dtypes.int32)
      q.enqueue(37)
  
      @def_function.function
      def f():
        return q.dequeue()
  
      c_mgr = cancellation.CancellationManager()
      cancelable_func = c_mgr.get_cancelable_function(f.get_concrete_function())
  
      self.assertAllEqual(37, cancelable_func().numpy())
  
      # Cancellation after the function executes is a no-op.
      c_mgr.start_cancel()
  
    def testAddFunctionCallback(self):
      functions = []
      def function_callback(f, name, graph, inputs, outputs):
        del name, graph, inputs, outputs
        functions.append(f)
  
      @def_function.function
      def plus_one(x):
        return x + 1
  
      try:
        function.add_function_callback(function_callback)
        x_float32 = numpy.array(3.0, dtype=numpy.float32)
        self.assertAllClose(plus_one(x_float32), 4.0)
        self.assertLen(functions, 1)
        # Function is already created. Executing it again should not invoke the
        # function callback.
        self.assertAllClose(plus_one(x_float32), 4.0)
        self.assertLen(functions, 1)
        # Signature change leads to a new Function being built.
        x_float64 = numpy.array(3.0, dtype=numpy.float64)
        self.assertAllClose(plus_one(x_float64), 4.0)
        self.assertLen(functions, 2)
      finally:
        function.clear_function_callbacks()
  
    def testFunctionCallbackAddOps(self):
      file_name = os.path.join(self.get_temp_dir(), 'test')
  
      def function_callback(f, name, graph, inputs, outputs):
        del f, name, inputs
  
        with graph.as_default():
          printer = logging_ops.print_v2(
              'hello',
              output_stream='file://' + file_name
          )
          outputs[0].op._add_control_input(printer)
  
      @def_function.function
      def plus_one(x):
        return x + 1
  
      self.addCleanup(function.clear_function_callbacks)
      function.add_function_callback(function_callback)
      x_float32 = numpy.array(3.0, dtype=numpy.float32)
  
      self.assertAllClose(plus_one(x_float32), 4.0)
  
      with open(file_name, 'r') as f:
        self.assertEqual(f.read().strip(), 'hello')
  
    def testRemoveFunctionCallback(self):
      functions_1 = []
      def function_callback_1(f, name, graph, inputs, outputs):
        del name, graph, inputs, outputs
        functions_1.append(f)
  
      functions_2 = []
      def function_callback_2(f, name, graph, inputs, outputs):
        del name, graph, inputs, outputs
        functions_2.append(f)
  
      @def_function.function
      def plus_one(x):
        return x + 1
  
      try:
        function.add_function_callback(function_callback_1)
        function.add_function_callback(function_callback_2)
        self.assertAllClose(plus_one(numpy.array(3.0, dtype=numpy.float32)), 4.0)
        self.assertLen(functions_1, 1)
        self.assertLen(functions_2, 1)
        function.remove_function_callback(function_callback_1)
        # The 1st callback should not be invokved after remove_function_callback()
        # is called.
        self.assertAllClose(plus_one(numpy.array(3.0, dtype=numpy.float64)), 4.0)
        self.assertLen(functions_1, 1)
        self.assertLen(functions_2, 2)
      finally:
        function.clear_function_callbacks()
  
    def testClearFunctionCallbacks(self):
      function.add_function_callback(lambda f: None)
      function.add_function_callback(lambda f: None)
      self.assertLen(function._function_callbacks, 2)
      function.clear_function_callbacks()
      self.assertEmpty(function._function_callbacks)  # pylint:disable=protected-access
  
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionWithNestedTensorInputs(self):
  
      @def_function.function
      def f(x, y):
        return (x['a'] + x['b'], y[0] + y[1])
  
      a = constant_op.constant(1000)
      b = constant_op.constant(200)
      c = constant_op.constant(30)
      d = {'a': a, 'b': b}
      e = (c, 4)
  
      # Test different argument signatures when constructing the concrete func.
      for cf in [
          f.get_concrete_function(d, e),
          f.get_concrete_function(d, y=e),
          f.get_concrete_function(y=e, x=d),
          f.get_concrete_function(_spec_for_value(d), _spec_for_value(e)),
          f.get_concrete_function(_spec_for_value(d), y=_spec_for_value(e)),
          f.get_concrete_function(y=_spec_for_value(e), x=_spec_for_value(d))
      ]:
        # Test different calling conventions when calling the concrete func.
        for output in [
            cf(d, e),  # structured signature
            cf(d, y=e),  # structured signature w/ kwarg
            cf(y=e, x=d),  # structured signature w/ 2 kwargs
            cf(a, b, c),  # flat signature
            cf(x=a, x_1=b, y=c)  # flat signature w/ kwargs
        ]:
          self.assertIsInstance(output, tuple)
          self.assertLen(output, 2)
          self.assertAllEqual(output[0], 1200)
          self.assertAllEqual(output[1], 34)
  
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionWithNestedNonTensorInputs(self):
  
      @def_function.function
      def f(x, y):
        return (x['a'] + x['b'], y[0] + y[1])
  
      a = {'a': constant_op.constant(1000), 'b': constant_op.constant(200)}
      b = (50, 3)
  
      for cf in [  # argument y is bound to non-Tensor value (50, 3).
          f.get_concrete_function(a, b),
          f.get_concrete_function(a, y=b),
          f.get_concrete_function(x=a, y=b)
      ]:
        for output in [cf(a), cf(x=a), cf(a, b), cf(x=a, y=b)]:
          self.assertAllEqual(output[0] + output[1], 1253)
  
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionWithNonTensorStringInputs(self):
  
      @def_function.function
      def f(x, y):
        return string_ops.string_join([x, y])
  
      a = constant_op.constant('a')
      b = 'b'
  
      cf = f.get_concrete_function(a, b)
      for output in [cf(a), cf(x=a), cf(a, b), cf(x=a, y=b)]:
        self.assertAllEqual(output, b'ab')
  
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionWithBoundNestedNonTensorInputs(self):
  
      @def_function.function
      def f(x, y):
        return (x['a'] + x['b'], y[0] + y[1])
  
      a = {'a': 3000, 'b': 200, 'c': 9000}
      b = (constant_op.constant(30), 4)
  
      for cf in [  # argument x is bound to non-tensor value `a`
          f.get_concrete_function(a, b),
          f.get_concrete_function(a, y=b),
          f.get_concrete_function(x=a, y=b)
      ]:
        for output in [cf(a, b), cf(a, y=b), cf(y=b), cf(x=a, y=b)]:
          self.assertAllEqual(output[0] + output[1], 3234)
  
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionWithAllBoundNestedNonTensorInputs(self):
  
      @def_function.function
      def f(x, y):
        return (x['a'] + x['b'], y[0] + y[1])
  
      a = {'a': 5000, 'b': 500}
      b = (50, 5)
  
      cf = f.get_concrete_function(a, b)
      for output in [cf(), cf(a), cf(y=b)]:
        self.assertAllEqual(output[0] + output[1], 5555)
  
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionMethodWithVarargs(self):
      float32_scalar = tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32)
  
      class MyModel(module.Module):
  
        @def_function.function(input_signature=[float32_scalar, float32_scalar])
        def add(self, *arg):
          return math_ops.add(*arg)
  
      m = MyModel()
      cf = m.add.get_concrete_function()
      cf(-12.0, 3.0)
  
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionStructuredSignatureKeywordOrder(self):
      # Check that keyword-only arguments are sorted appropriately, so that they
      # feed the right tensor into each input.
      @def_function.function
      def g(**kwargs):
        return string_ops.reduce_join(
            string_ops.reduce_join(
                ops.convert_to_tensor(sorted(kwargs.items())),
                axis=1,
                separator='='),
            axis=0,
            separator=', ')
  
      s = constant_op.constant('s')
      g.get_concrete_function(q=s, a=s, p=s, r=s, v=s, m=s, l=s)
      self.assertAllEqual(
          g(m='a', r='b', v='c', q='d', l='e', a='f', p='g'),
          b'a=f, l=e, m=a, p=g, q=d, r=b, v=c')
      self.assertAllEqual(
          g(q='d', a='f', p='g', r='b', v='c', m='a', l='e'),
          b'a=f, l=e, m=a, p=g, q=d, r=b, v=c')
      self.assertAllEqual(
          g(a='f', l='e', m='a', p='g', q='d', r='b', v='c'),
          b'a=f, l=e, m=a, p=g, q=d, r=b, v=c')
  
    # pylint: disable=g-long-lambda
    @parameterized.named_parameters([
        dict(
            testcase_name='MissingArg',
            conc_args=lambda: (1, constant_op.constant(2)),
            call_args=lambda: (1,),
            error=r'func\(x, y\) missing required arguments: y'),
        dict(
            testcase_name='MissingVararg',
            conc_args=lambda: (1, 2, constant_op.constant(1.0)),
            call_args=lambda: (1, 2),
            error=r'func\(x, y, <arg3>\) missing required arguments: <arg3>'),
        dict(
            testcase_name='ExtraPositionalArg',
            conc_args=lambda: (1, 2),
            call_args=lambda: (1, 2, 3),
            error=r'func\(x, y\) takes 2 .* got 3'),
        dict(
            testcase_name='MissingKeywordOnlyArg',
            conc_args=lambda: (1, 2),
            conc_kwargs=lambda: {'c': constant_op.constant(1.0)},
            call_args=lambda: (1, 2),
            error=r'func\(x, y, \*, c\) missing required arguments: c'),
        dict(
            testcase_name='ExtraKeywordArg',
            conc_args=lambda: (1, 2),
            call_args=lambda: (1, 2),
            call_kwargs=lambda: {'c': constant_op.constant(1.0)},
            error=r'func\(x, y\) got unexpected keyword arguments: c'),
        dict(
            testcase_name='ExpectedRaggedGotNest',
            conc_args=lambda: (ragged_factory_ops.constant([[1, 2], [3]]),),
            call_args=lambda: ({
                'a': constant_op.constant([1, 2, 3])
            },),
            error=r'func\(x, y\): argument x had incorrect type\n'
            r'  expected: RaggedTensor\n'
            r"       got: {'a': (Eager)?Tensor}"),
        dict(
            testcase_name='WrongRaggedRank',
            conc_args=lambda: (ragged_factory_ops.constant([[1, 2], [3]]),),
            call_args=lambda: (ragged_factory_ops.constant([[[1]]]),),
            error=r'func\(x, y\): argument x had incorrect type\n'),
        dict(
            testcase_name='WrongRaggedDType',
            conc_args=lambda: (ragged_factory_ops.constant([[1]]),),
            call_args=lambda: (ragged_factory_ops.constant([[1.0]]),),
            error=r'func\(x, y\): argument x had incorrect type\n'),
        dict(
            testcase_name='ExpectedDictGotTensor',
            conc_args=lambda: ({
                'a': constant_op.constant(1),
                'b': constant_op.constant(1)
            },),
            call_args=lambda: (constant_op.constant(1),),
            error=r'func\(x, y\): argument x had incorrect type\n'),
        dict(
            testcase_name='ExpectedTupleGotTensor',
            conc_args=lambda:
            ((constant_op.constant(1), constant_op.constant(2)),),
            call_args=lambda: (constant_op.constant(1),),
            error=r'func\(x, y\): argument x had incorrect type\n'),
        dict(
            testcase_name='WrongDType',
            conc_args=lambda: (constant_op.constant(1),),
            call_args=lambda: (constant_op.constant(1.0),),
            exception=(ValueError, errors.InvalidArgumentError,
                       # on xla_gpu, we get InternalError instead.
                       errors.InternalError)),
        dict(
            testcase_name='ExpectedTensorGotInt',
            conc_args=lambda: (constant_op.constant(1),),
            call_args=lambda: (5,),
            error=r'func\(x, y\) expected a Tensor in x, but got int value 5'),
        dict(
            testcase_name='ExpectedIntGotDifferentInt',
            conc_args=lambda: (5,),
            call_args=lambda: (8,),
            error=r'ConcreteFunction func\(x, y\) was constructed with int '
            r'value 5 in x, but was called with int value 8'),
        dict(
            testcase_name='ExpectedIntGotTensor',
            conc_args=lambda: (5,),
            call_args=lambda: (constant_op.constant(6),),
            error=r'ConcreteFunction func\(x, y\) was constructed with int '
            'value 5 in x, but was called with (Eager)?Tensor value .*'),
        dict(
            testcase_name='TwoValuesForArgument',
            conc_args=lambda: (1, 2),
            call_args=lambda: (1, 2),
            call_kwargs=lambda: {'x': 3},
            error=r"func\(x, y\) got two values for 'x'"),
    ])
    # pylint: enable=g-long-lambda
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionStructuredSignatureError(self,
                                                     conc_args=(),
                                                     conc_kwargs=None,
                                                     call_args=(),
                                                     call_kwargs=None,
                                                     error='.*',
                                                     exception=TypeError):
      """Tests for errors in the structrued signature.
  
      Args:
        conc_args: Positional arguments used for get_concrete_function.
        conc_kwargs: Keyword arguments used for get_concrete_function.
        call_args: Positional arguments used to call the function.
        call_kwargs: Keyword arguments used to call the function.
        error: Expected exception message.
        exception: Expected exception type.
      """
      conc_args = conc_args() if callable(conc_args) else conc_args
      conc_kwargs = conc_kwargs() if callable(conc_kwargs) else conc_kwargs or {}
      call_args = call_args() if callable(call_args) else call_args
      call_kwargs = call_kwargs() if callable(call_kwargs) else call_kwargs or {}
      self.assertIsInstance(conc_args, tuple)
      self.assertIsInstance(call_args, tuple)
      self.assertIsInstance(conc_kwargs, dict)
      self.assertIsInstance(call_kwargs, dict)
  
      @def_function.function
      def func(x, y=5, *varargs, **kwargs):  # pylint: disable=keyword-arg-before-vararg
        del y, varargs, kwargs
        return x
  
      conc = func.get_concrete_function(*conc_args, **conc_kwargs)
      with self.assertRaisesRegex(exception, error):
        self.evaluate(conc(*call_args, **call_kwargs))
  
    # pylint: disable=g-long-lambda
    @parameterized.named_parameters([
        dict(
            testcase_name='MissingArg',
            conc_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            call_args=lambda: (constant_op.constant(1),),
            error=r'func\(x, y\) missing required arguments: y'),
        dict(
            testcase_name='TwoValuesForArg',
            conc_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            call_args=lambda: (constant_op.constant(1),),
            call_kwargs=lambda: {
                'x': constant_op.constant(1),
                'y': constant_op.constant(1)
            },
            error=r"func\(x, y\) got two values for 'x'"),
        dict(
            testcase_name='ExtraPositionalArg',
            conc_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            call_args=lambda: (constant_op.constant(1), constant_op.constant(2),
                               constant_op.constant(3)),
            error=r'func\(x, y\) takes 2 .* got 3'),
        dict(
            testcase_name='UnexpectedKeywordArg',
            conc_args=lambda: (constant_op.constant(1),),
            call_args=lambda: (constant_op.constant(1),),
            call_kwargs=lambda: {'c': constant_op.constant(1)},
            error=r'func\(x\) got unexpected keyword arguments: c'),
        dict(
            testcase_name='MissingVararg',
            conc_args=lambda: (constant_op.constant(1), constant_op.constant(2),
                               constant_op.constant(3)),
            call_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            error=r'func\(x, y, varargs_0\) missing required '
            r'arguments: varargs_0'),
        dict(
            testcase_name='MissingKeywordArg',
            conc_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            conc_kwargs=lambda: {'c': constant_op.constant(1)},
            call_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            error=r'func\(x, y, c\) missing required arguments: c'),
        dict(
            testcase_name='ExpectedTensorGotInt',
            conc_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            call_args=lambda: (5, constant_op.constant(2)),
            error=r'func\(x, y\): expected argument #0\(zero-based\) to be '
            r'a Tensor; got int \(5\)'),
        dict(
            testcase_name='WrongDType',
            conc_args=lambda: (constant_op.constant(1),),
            call_args=lambda: (constant_op.constant(1.0),),
            exception=(ValueError, errors.InvalidArgumentError,
                       # on xla_gpu, we get InternalError instead.
                       errors.InternalError)),
        dict(
            testcase_name='MissingKeywordArgNestPiece',
            conc_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            conc_kwargs=lambda: {'c': ragged_factory_ops.constant([[1]])},
            call_args=lambda: (constant_op.constant(1), constant_op.constant(2)),
            call_kwargs=lambda: {'c': constant_op.constant(1)},
            error=r'func\(x, y, c, c_1\) missing required arguments: c_1'),
    ])
    # pylint: enable=g-long-lambda
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionFlatSignatureError(self,
                                               conc_args=(),
                                               conc_kwargs=None,
                                               call_args=(),
                                               call_kwargs=None,
                                               error='.*',
                                               exception=TypeError):
      """Tests for errors in the flat signature.
  
      Args:
        conc_args: Positional arguments used for get_concrete_function.
        conc_kwargs: Keyword arguments used for get_concrete_function.
        call_args: Positional arguments used to call the function.
        call_kwargs: Keyword arguments used to call the function.
        error: Expected exception message.
        exception: Expected exception type.
      """
      conc_args = conc_args() if callable(conc_args) else conc_args
      conc_kwargs = conc_kwargs() if callable(conc_kwargs) else conc_kwargs or {}
      call_args = call_args() if callable(call_args) else call_args
      call_kwargs = call_kwargs() if callable(call_kwargs) else call_kwargs or {}
      self.assertIsInstance(conc_args, tuple)
      self.assertIsInstance(call_args, tuple)
      self.assertIsInstance(conc_kwargs, dict)
      self.assertIsInstance(call_kwargs, dict)
  
      @def_function.function
      def func(x, y=5, *varargs, **kwargs):  # pylint: disable=keyword-arg-before-vararg
        del y, varargs, kwargs
        return x
  
      conc = func.get_concrete_function(*conc_args, **conc_kwargs)
  
      # Remove _function_spec, to disable the structured signature.
      conc._set_function_spec(None)  # pylint: disable=protected-access
  
      with self.assertRaisesRegex(exception, error):
        self.evaluate(conc(*call_args, **call_kwargs))
  
    @test_util.run_in_graph_and_eager_modes
    def testConcreteFunctionAmbiguousSignature(self):
      # When both the flat & structured signatures are applicable, but they
      # give different results, we use the structured signature.  Note: we expect
      # this to be extremely rare.
      @def_function.function
      def f(x, y):
        return x * 10 + y
  
      conc = f.get_concrete_function(
          x=tensor_spec.TensorSpec(None, dtypes.int32, name='y'),
          y=tensor_spec.TensorSpec(None, dtypes.int32, name='x'))
  
      result = conc(x=constant_op.constant(5), y=constant_op.constant(6))
      self.assertAllEqual(result, 56)
  
    def testPrettyPrintedSignature(self):
  
      @def_function.function
      def func(x, kangaroo=None, octopus=7):
        del octopus, kangaroo
        return x
  
      scalar = constant_op.constant(5)
      vector = constant_op.constant([10, 10, 20])
      ragged = ragged_factory_ops.constant([[10, 20], [40]])
  
      c1 = func.get_concrete_function(scalar, vector)
      c1_summary = r'func\(x, kangaroo, octopus=7\)'
      c1_details = (r'  Args:\n'
                    r'    x: int32 Tensor, shape=\(\)\n'
                    r'    kangaroo: int32 Tensor, shape=\(3,\)\n'
                    r'  Returns:\n'
                    r'    int32 Tensor, shape=\(\)')
      self.assertRegex(c1.pretty_printed_signature(verbose=False), c1_summary)
      self.assertRegex(
          c1.pretty_printed_signature(verbose=True),
          c1_summary + '\n' + c1_details)
      self.assertRegex(
          repr(c1), r'<ConcreteFunction func\(x, kangaroo, octopus=7\) at .*>')
      self.assertRegex(
          str(c1), 'ConcreteFunction {}\n{}'.format(c1_summary, c1_details))
  
      c2 = func.get_concrete_function(scalar, ragged, 3)
      c2_summary = r'func\(x, kangaroo, octopus=3\)'
      c2_details = (r'  Args:\n'
                    r'    x: int32 Tensor, shape=\(\)\n'
                    r'    kangaroo: RaggedTensorSpec\(.*\)\n'
                    r'  Returns:\n'
                    r'    int32 Tensor, shape=\(\)')
      self.assertRegex(c2.pretty_printed_signature(),
                       c2_summary + '\n' + c2_details)
  
      c3 = func.get_concrete_function({'a': scalar, 'b': [ragged, ragged]})
      c3_summary = r'func\(x, kangaroo=None, octopus=7\)'
      c3_details = (r'  Args:\n'
                    r"    x: {'a': <1>, 'b': \[<2>, <3>\]}\n"
                    r'      <1>: int32 Tensor, shape=\(\)\n'
                    r'      <2>: RaggedTensorSpec\(.*\)\n'
                    r'      <3>: RaggedTensorSpec\(.*\)\n'
                    r'  Returns:\n'
                    r"    {'a': <1>, 'b': \[<2>, <3>\]}\n"
                    r'      <1>: int32 Tensor, shape=\(\)\n'
                    r'      <2>: RaggedTensorSpec\(.*\)\n'
                    r'      <3>: RaggedTensorSpec\(.*\)')
  
      # python 3.5 does not gurantee deterministic iteration of dict contents
      # which can lead mismatch on pretty_printed_signature output for "Args"
      if sys.version_info >= (3, 6):
        self.assertRegex(c3.pretty_printed_signature(),
                         c3_summary + '\n' + c3_details)
  
      # pylint: disable=keyword-arg-before-vararg
      @def_function.function
      def func2(x, y=3, *args, **kwargs):
        return (x, y, args, kwargs)
  
      c4 = func2.get_concrete_function(scalar, 4, 5, a=scalar)
      c4_summary = 'func2(x, y=4, <arg3>=5, *, a)'
      self.assertEqual(c4.pretty_printed_signature(verbose=False), c4_summary)
  
      c5 = func2.get_concrete_function(8, vector)
      c5_summary = 'func2(x=8, y)'
      self.assertEqual(c5.pretty_printed_signature(verbose=False), c5_summary)
  
    def testPrettyPrintedExplicitSignatureWithKeywordArg(self):  # b/159639913
  
      @def_function.function(input_signature=[tensor_spec.TensorSpec(None)])
      def fn(a, b=1):
        return a + b
  
      concrete_fn = fn.get_concrete_function()
      self.assertEqual(concrete_fn.pretty_printed_signature(False), 'fn(a)')
      self.assertEqual(
          concrete_fn.pretty_printed_signature(True), 'fn(a)\n'
          '  Args:\n'
          '    a: float32 Tensor, shape=<unknown>\n'
          '  Returns:\n'
          '    float32 Tensor, shape=<unknown>')
  
    def testPrettyPrintedSignatureLoadedNamedTuple(self):
      Point = collections.namedtuple('Point', ['x', 'y'])
  
      @def_function.function
      def fn(b, a):  # pylint: disable=unused-argument
        return 1.
  
      b = Point(
          x=constant_op.constant(1., dtype=dtypes.float32),
          y=constant_op.constant(1., dtype=dtypes.float32))
      a = Point(
          x=constant_op.constant(1, dtype=dtypes.int32),
          y=constant_op.constant(1, dtype=dtypes.int32))
  
      mod = module.Module()
      f = fn.get_concrete_function(b, a)
      save(mod, '/tmp/f', signatures=f)
      loaded = load('/tmp/f')
  
      printed = loaded.signatures['serving_default'].pretty_printed_signature()
      self.assertIn('a: int32 Tensor, shape=()', printed)
      self.assertIn('a_1: int32 Tensor, shape=()', printed)
      self.assertIn('b: float32 Tensor, shape=()', printed)
      self.assertIn('b_1: float32 Tensor, shape=()', printed)
  
    @test_util.run_in_graph_and_eager_modes
    def testIndexedSlicesAsGradientsForConcreteFunctions(self):
  
      @def_function.function
      def summing_rnn(inputs):
        return math_ops.reduce_sum(inputs, axis=1)
  
      @def_function.function
      def gradients(inputs):
        with backprop.GradientTape() as tape:
          tape.watch(inputs)
          hidden = summing_rnn(inputs)
          hidden = array_ops.gather(hidden, constant_op.constant([0]))
          loss = math_ops.reduce_mean(hidden)
        return tape.gradient(loss, inputs)
  
      gradients(constant_op.constant([[[1.0], [2.0]]]))  # No error is raised
  
    def testFollowTypeHintsTraceBasic(self):
      trace_count = [0]
  
      def func(x: ops.Tensor):
        trace_count[0] += 1
        return x
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
      disabled = def_function.function(func, experimental_follow_type_hints=False)
  
      enabled(1)  # Initial call gets traced
      enabled(2)
      enabled(3)
      self.assertEqual(trace_count[0], 1)
  
      trace_count = [0]
      disabled(1)
      disabled(2)  # Retrace
      disabled(3)  # Retrace
      self.assertEqual(trace_count[0], 3)
  
    def testFollowTypeHintsTraceWithArgs(self):
      trace_count = [0]
  
      def func(*args: ops.Tensor):
        trace_count[0] += 1
        return args
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
      disabled = def_function.function(func, experimental_follow_type_hints=False)
  
      args = (
          'abc',
          'def',
      ) * 20
      args2 = (
          'def',
          'abc',
      ) * 20
  
      enabled(args)
      enabled(args2)
      self.assertEqual(trace_count[0], 1)
  
      trace_count = [0]
      disabled(args)
      disabled(args2)  # Retrace
      self.assertEqual(trace_count[0], 2)
  
    def testFollowTypeHintsTraceWithKwargs(self):
      trace_count = [0]
  
      def func(t: ops.Tensor, **kwargs: ops.Tensor):
        del kwargs
        trace_count[0] += 1
        return t
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
      disabled = def_function.function(func, experimental_follow_type_hints=False)
  
      enabled(1, x=1, y=1.0, z='one')
      enabled(2, x=2, y=2.0, z='two')
      self.assertEqual(trace_count[0], 1)
  
      trace_count = [0]
      disabled(1, x=1, y=1.0, z='one')
      disabled(2, x=2, y=2.0, z='two')  # Retrace
      self.assertEqual(trace_count[0], 2)
  
    def testFollowTypeHintsTraceWithMultipleInputTypes(self):
      trace_count = [0]
  
      def func(t: ops.Tensor, *args: ops.Tensor, **kwargs: ops.Tensor):
        del args, kwargs
        trace_count[0] += 1
        return t
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
      disabled = def_function.function(func, experimental_follow_type_hints=False)
  
      enabled(1, constant_op.constant(1), 'str', x=4.0)
      enabled(2, constant_op.constant(2), 'str2', x=5.0)
      self.assertEqual(trace_count[0], 1)
  
      trace_count = [0]
      disabled(1, constant_op.constant(1), 'str', x=4.0)
      disabled(2, constant_op.constant(2), 'str2', x=5.0)  # Retrace
      self.assertEqual(trace_count[0], 2)
  
    def testFollowTypeHintsTraceWithOnlyArgNamed(self):
      trace_count = [0]
  
      def func(t: ops.Tensor, i: int = 1, **kwargs):  # pylint: disable=bad-whitespace
        del i, kwargs
        trace_count[0] += 1
        return t
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(1, 3, x=4.0, y='str')
      enabled(2, 4, x=4.0, y='str')  # Retrace
      self.assertEqual(trace_count[0], 2)
  
    def testFollowTypeHintsTraceWithNotAllNamed(self):
      trace_count = [0]
  
      def func(x, y: ops.Tensor, z: int):
        del y, z
        trace_count[0] += 1
        return x
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(1, 2, 3)
      enabled(1, 20, 3)  # No retrace - change in ops.Tensor typed arg
      enabled(2, 2, 3)  # Retrace - change in untyped arg
      enabled(2, 2, 4)  # Retrace - change in typed arg
      self.assertEqual(trace_count[0], 3)
  
    def testFollowTypeHintsTraceWithOnlyArgsNamed(self):
      trace_count = [0]
  
      def func(x, y, *args: ops.Tensor):
        del y, args
        trace_count[0] += 1
        return x
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(1, 20, 3, 4, 5, 6)
      enabled(1, 20, 3, 4, 5, 60)  # No retrace - change in *args
      enabled(1, 30, 7, 8, 9, 10)  # Retrace - change in args
      self.assertEqual(trace_count[0], 2)
  
    def testFollowTypeHintsTraceWithOnlyKwargsNamed(self):
      trace_count = [0]
  
      def func(x, y, *args, **kwargs: ops.Tensor):
        del y, args, kwargs
        trace_count[0] += 1
        return x
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(1, 2, 3, 4, 5, 6, a=1.0, b=2.0, c=3.0)
      enabled(
          1, 2, 3, 4, 5, 6, a=1.5, b=2.5,
          c=3.5)  # No retrace - change in **kwargs
      enabled(100, 2, 3, 4, 5, 6, a=1.0, b=2.0, c=3.0)  # Retrace - change in args
      enabled(
          1, 2, 3, 4, 5, 100, a=1.0, b=2.0, c=3.0)  # Retrace - change in *args
      self.assertEqual(trace_count[0], 3)
  
    def testFollowTypeHintsTraceWithArgsEquals(self):
      trace_count = [0]
  
      def func(
          x: ops.Tensor = 0,  # pylint:disable=bad-whitespace
          y: int = 1,  # pylint:disable=bad-whitespace
          **kwargs: ops.Tensor):
        del y, kwargs
        trace_count[0] += 1
        return x
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(x=1, y=2, z=3)
      enabled(x=1, y=3, z=3)  # Retrace - change in args
      enabled(x=2, y=2, z=4)  # No retrace - change in args and **kwargs
      enabled(x=2, y=2, z=4, u=5)  # Retrace - change in **kwargs
      self.assertEqual(trace_count[0], 3)
  
    def testFollowTypeHintsWithTensorSpec(self):
      def func(x: ops.Tensor, y):
        return x + y
      v = def_function.function(experimental_follow_type_hints=True)(func)
      v = v.get_concrete_function(
          tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32), 3)
      x = v(constant_op.constant(1.), 3)
      self.assertEqual(x.numpy(), 4.)
  
    def testFollowTypeHintsTraceWithKwArgsAndNoVarKws(self):
      trace_count = [0]
  
      def func(a: int, b: ops.Tensor,
               x: ops.Tensor = 0, y: int = 1):
        del a, b, y
        trace_count[0] += 1
        return x
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(0, 0, x=1, y=2)
      enabled(0, 0, x=2, y=2,)  # No retrace, since only tensor changed
      self.assertEqual(trace_count[0], 1)
  
      # Pass args as keyword args.
      enabled(a=0, b=0, x=2, y=2,)  # No retrace, args are the same
      self.assertEqual(trace_count[0], 1)
  
      enabled(a=1, b=0, x=2, y=2,)  # Retrace, since non-tensor arg changed
      self.assertEqual(trace_count[0], 2)
  
      enabled(a=1, b=2, x=2, y=2)  # No retrace, since only tensor changed
      self.assertEqual(trace_count[0], 2)
  
      trace_count[0] = 0
      disabled = def_function.function(func, experimental_follow_type_hints=False)
      disabled(0, 0, x=1, y=2)
      disabled(0, 0, x=2, y=2,)  # Retrace
      self.assertEqual(trace_count[0], 2)
  
    def testFollowTypeHintsTraceWithArgsEqualsTypedKwargs(self):
      trace_count = [0]
  
      def func(x, y, **kwargs: ops.Tensor):
        del y, kwargs
        trace_count[0] += 1
        return x
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(x=1, y=2, z=3)
      enabled(x=1, y=3, z=3)  # Retrace
      enabled(x=1, y=2, z=4)  # No retrace
      enabled(x=2, y=2, z=4)  # Retrace
      enabled(x=2, y=2, z=4, u=5)  # Retrace
      self.assertEqual(trace_count[0], 4)
  
    def testFollowTypeHintsTraceWithArgsEqualsTypedArgs(self):
      trace_count = [0]
  
      def func(x: ops.Tensor, y: int, **kwargs):
        del y, kwargs
        trace_count[0] += 1
        return x
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(x=1, y=2, z=3)
      enabled(x=1, y=3, z=3)  # Retrace
      enabled(x=1, y=2, z=4)  # Retrace
      enabled(x=2, y=2, z=3)  # No retrace
      enabled(x=2, y=2, z=4, u=5)  # Retrace
      self.assertEqual(trace_count[0], 4)
  
    def testFollowTypeHintsTraceWithKwOnlyArgsBasic(self):
      trace_count = [0]
  
      def func(*, a: ops.Tensor = None, b=1):  # pylint: disable=bad-whitespace
        del b
        trace_count[0] += 1
        return a
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(a=1, b=2)
      enabled(a=2, b=2)  # No retrace
      enabled(a=1, b=1)  # Retrace
      self.assertEqual(trace_count[0], 2)
  
    def testFollowTypeHintsTraceWithArgsKwOnlyArgsKwargsAndTypedArg(self):
      trace_count = [0]
  
      def func(arg: ops.Tensor, *args, kwonly, **kwargs):
        del args, kwonly, kwargs
        trace_count[0] += 1
        return arg
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)
      enabled(100, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)  # No retrace
      enabled(1000, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)  # No retrace
      enabled(1, 20, 30, 40, kwonly=5, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 2, 3, 4, kwonly=50, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=60, kwarg2=70)  # Retrace
      self.assertEqual(trace_count[0], 4)
  
    def testFollowTypeHintsTraceWithArgsKwOnlyArgsKwargsAndTypedArgs(self):
      trace_count = [0]
  
      def func(arg, *args: ops.Tensor, kwonly, **kwargs):
        del args, kwonly, kwargs
        trace_count[0] += 1
        return arg
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)
      enabled(100, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 20, 30, 40, kwonly=5, kwarg1=6, kwarg2=7)  # No retrace
      enabled(1, 200, 300, 400, kwonly=5, kwarg1=6, kwarg2=7)  # No retrace
      enabled(1, 2, 3, 4, kwonly=50, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=60, kwarg2=70)  # Retrace
      self.assertEqual(trace_count[0], 4)
  
    def testFollowTypeHintsTraceWithArgsKwOnlyArgsKwargsAndTypedKwOnlyArg(self):
      trace_count = [0]
  
      def func(arg, *args, kwonly: ops.Tensor, **kwargs):
        del args, kwonly, kwargs
        trace_count[0] += 1
        return arg
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)
      enabled(100, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 20, 30, 40, kwonly=5, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 2, 3, 4, kwonly=50, kwarg1=6, kwarg2=7)  # No retrace
      enabled(1, 2, 3, 4, kwonly=500, kwarg1=6, kwarg2=7)  # No retrace
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=60, kwarg2=70)  # Retrace
      self.assertEqual(trace_count[0], 4)
  
    def testFollowTypeHintsTraceWithArgsKwOnlyArgsKwargsAndTypedKwargs(self):
      trace_count = [0]
  
      def func(arg, *args, kwonly, **kwargs: ops.Tensor):
        del args, kwonly, kwargs
        trace_count[0] += 1
        return arg
  
      enabled = def_function.function(func, experimental_follow_type_hints=True)
  
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)
      enabled(100, 2, 3, 4, kwonly=5, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 20, 30, 40, kwonly=5, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 2, 3, 4, kwonly=50, kwarg1=6, kwarg2=7)  # Retrace
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=60, kwarg2=70)  # No retrace
      enabled(1, 2, 3, 4, kwonly=5, kwarg1=600, kwarg2=700)  # No retrace
      self.assertEqual(trace_count[0], 4)
  
    def testWithExtraWrapper(self):
  
      class Foo(module.Module):
  
        def __init__(self):
          super().__init__()
          self.var = None
  
        @def_function.function
        @dummy_tf_decorator
        def add(self, x, y, z=1):
          if self.var is None:
            return x + y + z
  
      foo = Foo()
      self.assertEqual(foo.add(2, 3).numpy(), 6)
  
    @parameterized.parameters([(def_function.function, dummy_tf_decorator),
                               (dummy_tf_decorator, def_function.function),
                               (def_function.function, def_function.function)])
    def testWithExtraWrapperRedundantArgs(self, decorator1, decorator2):
  
      class Foo(module.Module):
  
        def __init__(self):
          super().__init__()
          self.var = None
  
        @decorator1
        @decorator2
        def add1(self, x, y):
          if self.var is None:
            return x + y
  
      foo = Foo()
      with self.assertRaisesRegex(TypeError, 'got two values'):
        foo.add1(2, x=3)  # pylint: disable=redundant-keyword-arg,no-value-for-parameter
  
    def testWithExtraWrapperMissingArgs(self):
  
      class Foo(module.Module):
  
        def __init__(self):
          super().__init__()
          self.var = None
  
        @def_function.function
        @dummy_tf_decorator
        def add1(self, x, y):
          if self.var is None:
            return x + y
  
        @def_function.function
        @dummy_tf_decorator
        def add2(self, x, y):
          if self.var is None:
            return x + y
  
        @def_function.function
        @def_function.function
        def add3(self, x, y):
          if self.var is None:
            return x + y
  
      foo = Foo()
      with self.assertRaisesRegex(
          TypeError, 'missing 1 required positional argument: \'y\''):
        foo.add1(2)  # pylint: disable=no-value-for-parameter
  
      with self.assertRaisesRegex(TypeError, 'missing 1 required argument: x'):
        foo.add1(y=2)  # pylint: disable=no-value-for-parameter
  
      with self.assertRaisesRegex(
          TypeError, 'missing 1 required positional argument: \'y\''):
        foo.add2(2)  # pylint: disable=no-value-for-parameter
  
      with self.assertRaisesRegex(TypeError, 'missing 1 required argument: x'):
        foo.add2(y=2)  # pylint: disable=no-value-for-parameter
  
      with self.assertRaisesRegex(
          TypeError, 'missing 1 required positional argument: \'y\''):
        foo.add3(2)  # pylint: disable=no-value-for-parameter
  
      with self.assertRaisesRegex(TypeError, 'missing 1 required argument: x'):
        foo.add3(y=2)  # pylint: disable=no-value-for-parameter
  
    def testMissingArgsTfFunctionedMethod(self):
  
      class A(object):
  
        def func(self, position_arg1, position_arg2):
          return position_arg1, position_arg2
  
        @def_function.function
        def decorated_method(self, position_arg1, position_arg2):
          return position_arg1, position_arg2
  
      a_instance = A()
      tf_method_pos = def_function.function(a_instance.func)
      with self.assertRaisesRegex(
          TypeError, '.* missing 1 required argument: position_arg1'):
        tf_method_pos(position_arg2='foo')
  
      # tf.function-decorated instance methods need to be tested because of
      # the __get__ method implementation.
      tf_func_decorated_method = def_function.function(
          a_instance.decorated_method)
      tf_func_decorated_method(position_arg1='foo', position_arg2='bar')
      with self.assertRaisesRegex(
          TypeError, '.* missing 1 required argument: position_arg1'):
        tf_func_decorated_method(position_arg2='bar')
  
    def testMissingArgsTfFunctionedObject(self):
  
      class A(object):
  
        def __call__(self, position_arg1, position_arg2):
          return position_arg1, position_arg2
  
      a_instance = A()
  
      # A tf.function-decorated callable object needs to be tested because of
      # the special inspect results.
      tf_func_obj = def_function.function(a_instance)
      tf_func_obj(position_arg1=1, position_arg2=2)
      with self.assertRaisesRegex(
          TypeError, '.* missing 1 required argument: position_arg1'):
        tf_func_obj(position_arg2='bar')
  
    def testMissingArgsTfFunctionedFunctions(self):
  
      def func_pos(position_arg1, position_arg2):
        return position_arg1, position_arg2
  
      def func_with_default(position_arg, named_arg=None):
        return position_arg, named_arg
  
      def func_pos_3args(position_arg1, position_arg2, position_arg3):
        return position_arg1, position_arg2, position_arg3
  
      tf_func_pos = def_function.function(func_pos)
      with self.assertRaisesRegex(
          TypeError, '.* missing 1 required argument: position_arg1'):
        tf_func_pos(position_arg2='foo')
  
      tf_func_with_default = def_function.function(func_with_default)
      tf_func_with_default(position_arg='bar')
      with self.assertRaisesRegex(TypeError,
                                  '.* missing 1 required argument: position_arg'):
        tf_func_with_default(named_arg='foo')
  
      tf_func_pos_3args = def_function.function(func_pos_3args)
      with self.assertRaisesRegex(
          TypeError,
          '.* missing required arguments: position_arg1, position_arg3'):
        tf_func_pos_3args(position_arg2='foo')
  
    def testShapeInferencePropagateConstNestedStack(self):
  
      @def_function.function(input_signature=[
          tensor_spec.TensorSpec((None, None), dtype=dtypes.int32),
          tensor_spec.TensorSpec((), dtype=dtypes.int32),
      ])
      def f(x, s):
        old_shape = array_ops.shape(x)
        new_shape = array_ops.stack([old_shape[0], s], axis=0)
        y = array_ops.ones(shape=new_shape, dtype=dtypes.int32)
        return y
  
      @def_function.function(input_signature=[
          tensor_spec.TensorSpec(shape=(3, 6), dtype=dtypes.int32)
      ])
      def g(x):
        y = f(x, s=5)
        assert y.shape.as_list() == [3, 5], y.shape.as_list()
        return y
  
      self.assertAllEqual(
          g(array_ops.zeros([3, 6], dtype=dtypes.int32)), array_ops.ones([3, 5]))
  
    def testShapeInferencePropagateConstNestedUnstackStack(self):
  
      @def_function.function(input_signature=[
          tensor_spec.TensorSpec((None, None), dtype=dtypes.int32),
          tensor_spec.TensorSpec((), dtype=dtypes.int32),
      ])
      def f(x, s):
        s0, _ = array_ops.unstack(array_ops.shape(x), axis=0)
        new_shape = array_ops.stack([s0, s], axis=0)
        y = array_ops.ones(shape=new_shape, dtype=dtypes.int32)
        return y
  
      @def_function.function(input_signature=[
          tensor_spec.TensorSpec(shape=(3, 6), dtype=dtypes.int32)
      ])
      def g(x):
        y = f(x, s=5)
        assert y.shape.as_list() == [3, 5], y.shape.as_list()
        return y
  
      self.assertAllEqual(
          g(array_ops.zeros([3, 6], dtype=dtypes.int32)), array_ops.ones([3, 5]))
  
    def testShapeInferencePropagateConstNestedConcat(self):
  
      @def_function.function(input_signature=[
          tensor_spec.TensorSpec((), dtype=dtypes.int32),
          tensor_spec.TensorSpec((), dtype=dtypes.int32),
          tensor_spec.TensorSpec((), dtype=dtypes.int32),
      ])
      def f(d1, d2, d3):
        new_shape = array_ops.concat([[d1], [d2], [d3]], axis=-1)
        y = array_ops.ones(shape=new_shape, dtype=dtypes.int32)
        return y
  
      @def_function.function()
      def g():
        y = f(1, 2, 3)
        assert y.shape.as_list() == [1, 2, 3], y.shape.as_list()
        return y
  
      self.assertAllEqual(g(), array_ops.ones([1, 2, 3]))
  
    def testShapeInferencePropagateConstDoubleNested(self):
  
      @def_function.function(input_signature=[
          tensor_spec.TensorSpec((), dtype=dtypes.int32),
          tensor_spec.TensorSpec((), dtype=dtypes.int32),
          tensor_spec.TensorSpec((), dtype=dtypes.int32),
      ])
      def f(d1, d2, d3):
        new_shape = array_ops.concat([[d1], [d2], [d3]], axis=-1)
        y = array_ops.ones(shape=new_shape, dtype=dtypes.int32)
        return y
  
      @def_function.function()
      def g():
        y = def_function.function(f)(1, 2, 3)
        assert y.shape.as_list() == [1, 2, 3], y.shape.as_list()
        return y
  
      self.assertAllEqual(g(), array_ops.ones([1, 2, 3]))
  
    @test_util.run_v2_only
    def testControlDependencyAfterInline(self):
      v = variables.Variable(0.)
  
      @def_function.function
      def assign():
        return v.assign(1.)
  
      @def_function.function
      def assign_add():
        return v.assign_add(1.)
  
      @def_function.function
      def f():
        check_ops.assert_equal_v2(assign(), 1.)
        check_ops.assert_equal_v2(assign_add(), 2.)
  
      # We don't have a way to inspect the inlined graph in Python, so we run it
      # multiple times to have more confidence the dependency is correct.
      for _ in range(30):
        f()
  
    @test_util.run_v2_only
    def testReadInFuncWriteOutside(self):
      # Run many times since we are testing for a potential race condition.
      for _ in range(30):
        # pylint: disable=cell-var-from-loop
        v = variables.Variable(1.)
  
        @def_function.function
        def add_one():
          return v + 1.
  
        @def_function.function
        def get_v_plus_one():
          v_plus_one = add_one()
          v.assign_add(2.0)
          return v_plus_one
  
        self.assertAllEqual(get_v_plus_one(), 2.0)
  
    def testOpExpandErrorMessage(self):
  
      @def_function.function
      def test_fn():
        if array_ops.constant(False):
          return array_ops.constant(1)
        else:
          return script_ops.eager_py_func(
              func=lambda: array_ops.constant([2.]), inp=(), Tout=dtypes.int32)
  
      error_pattern = re.compile(r'originated from.*func=lambda', re.DOTALL)
      with self.assertRaisesRegex(errors.InvalidArgumentError, error_pattern):
        test_fn()
  
  
  class MultiDeviceTest(test.TestCase, parameterized.TestCase):
  
    @test_util.run_gpu_only
    def testMultiDeviceOutput(self):
      """Tests that functions can produce outputs on multiple devices."""
      @function.defun
      def func(a, b, transpose_a):
        with ops.device('/device:CPU:0'):
          m1 = math_ops.matmul(a, b, transpose_a=transpose_a)
        with ops.device('/device:GPU:0'):
          m2 = math_ops.matmul(a, b, transpose_a=transpose_a)
        return m1, m2
  
      t = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
      m1, m2 = func(t, t, transpose_a=True)
      self.assertAllEqual(m1.numpy(), [[10, 14], [14, 20]])
      self.assertRegex(m1.backing_device, 'CPU')
      self.assertAllEqual(m2.numpy(), [[10, 14], [14, 20]])
      self.assertRegex(m2.backing_device, 'GPU')
  
    @test_util.run_gpu_only
    def testEmptyBody(self):
      @function.defun
      def func(a, b):
        return b, a
  
      with ops.device('/device:CPU:0'):
        a = array_ops.identity(3.0)
      with ops.device('/device:GPU:0'):
        b = array_ops.identity(5.0)
  
      m1, m2 = func(a, b)
      self.assertAllEqual(m1.numpy(), 5.0)
      self.assertRegex(m1.backing_device, 'GPU')
      self.assertAllEqual(m2.numpy(), 3.0)
      self.assertRegex(m2.backing_device, 'CPU')
  
    @test_util.run_gpu_only
    def testMultiDeviceInt32(self):
      """Tests that multi-device functions can take and output INT32s.
  
      When an INT32 device tensor is fed into a function, it is copied to CPU
      by the eager runtime. The function sees all INT32 inputs on CPU.
  
      We set allocator attribute 'on_host' for INT32 outputs. They can be
      partitioned into the GPU component function, but will be allocated on
      CPU nevertheless.
  
      There is experimental support for `ints_on_device` in
      FunctionLibraryRuntime now. We can try that.
  
      """
      with ops.device('/device:CPU:0'):
        int_cpu = constant_op.constant(3, dtype=dtypes.int32)
        resource = resource_variable_ops.ResourceVariable(5, dtype=dtypes.int32)
      with ops.device('/device:GPU:0'):
        int_gpu = constant_op.constant(7, dtype=dtypes.int32)
  
      @function.defun
      def func(int_cpu, resource, int_gpu):
        with ops.device('/device:CPU:0'):
          m1 = int_cpu * resource + int_gpu
        with ops.device('/device:GPU:0'):
          # This computation will happen on GPU but m2 will be copied to CPU.
          m2 = int_gpu * resource + int_cpu + 1
        return m1, m2
  
      m1, m2 = func(int_cpu, resource, int_gpu)
      self.assertAllEqual(m1.numpy(), 22)
      self.assertRegex(m1.backing_device, 'CPU')
      self.assertAllEqual(m2.numpy(), 39)
      self.assertRegex(m2.backing_device, 'CPU')
  
      # flip arguments
      m1, m2 = func(int_gpu, resource, int_cpu)
      self.assertAllEqual(m1.numpy(), 38)
      self.assertRegex(m1.backing_device, 'CPU')
      self.assertAllEqual(m2.numpy(), 23)
      self.assertRegex(m2.backing_device, 'CPU')
  
    @test_util.run_gpu_only
    def testMultiDeviceColocateWith(self):
      """Tests that function's outputs respect colocation constraints."""
      @function.defun
      def func(a, b):
        with ops.colocate_with(a):
          ra = 2 * a
        with ops.colocate_with(b):
          rb = 3 * b
        return ra, rb
  
      devices = ['/device:CPU:0', '/device:GPU:0']
      for dev1, dev2 in itertools.product(devices, devices):
        with ops.device(dev1):
          a = array_ops.identity(1.0)
        with ops.device(dev2):
          b = array_ops.identity(10.0)
  
        ra, rb = func(a, b)
        self.assertEqual(ra.numpy(), 2.0)
        self.assertRegex(ra.backing_device, dev1)
        self.assertEqual(rb.numpy(), 30.0)
        self.assertRegex(rb.backing_device, dev2)
  
    @test_util.run_gpu_only
    def testMultiDeviceResources(self):
      with ops.device('/device:CPU:0'):
        c1 = resource_variable_ops.ResourceVariable(2.0)
        c2 = resource_variable_ops.ResourceVariable(7.0)
      with ops.device('/device:GPU:0'):
        g1 = resource_variable_ops.ResourceVariable(3.0)
        g2 = resource_variable_ops.ResourceVariable(5.0)
  
      @function.defun
      def func(resource1, resource2):
        with ops.device('/device:CPU:0'):
          result1 = resource1 * g2
        with ops.device('/device:GPU:0'):
          result2 = resource2 * c2
        return result1, result2
  
      r1, r2 = func(c1, g1)
      self.assertEqual(r1.numpy(), 10.0)
      self.assertRegex(r1.backing_device, 'CPU')
      self.assertEqual(r2.numpy(), 21.0)
      self.assertRegex(r2.backing_device, 'GPU')
  
      # Call with flipped inputs. Check that we look at resource's
      # device and reinstantiates the function when inputs' devices change.
      r1, r2 = func(g1, c1)
      self.assertEqual(r1.numpy(), 15.0)
      self.assertRegex(r1.backing_device, 'CPU')
      self.assertEqual(r2.numpy(), 14.0)
      self.assertRegex(r2.backing_device, 'GPU')
  
    @test_util.run_gpu_only
    def testOutputResources(self):
      with ops.device('/device:CPU:0'):
        c1 = resource_variable_ops.ResourceVariable(2.0)
      with ops.device('/device:GPU:0'):
        g1 = resource_variable_ops.ResourceVariable(3.0)
  
      @function.defun
      def func(resource1, resource2):
        with ops.device('/device:CPU:0'):
          result1 = resource1 * 5
        with ops.device('/device:GPU:0'):
          result2 = resource2 * 7
        return result1, resource1.handle, result2, resource2.handle
  
      r1, res1, r2, res2 = func(c1, g1)
      self.assertEqual(r1.numpy(), 10.0)
      self.assertRegex(r1.backing_device, 'CPU')
      self.assertEqual(r2.numpy(), 21.0)
      self.assertRegex(r2.backing_device, 'GPU')
  
      def check_handle(handle, expected_value):
        self.assertRegex(handle.backing_device, 'CPU')
        tensor = gen_resource_variable_ops.read_variable_op(
            handle, dtypes.float32)
        self.assertEqual(tensor.numpy(), expected_value)
  
      # Check that handles returned from functions are on CPU and an op using
      # the resource handle is correctly placed on the device backing the
      # resource.
      check_handle(res1, 2.0)
      check_handle(res2, 3.0)
  
      # Call with flipped inputs to make sure the same the function is
      # reinstantiated and eager runtime does not mess up the device assignment
      # for ops consuming handles returned from defuns.
      r1, res1, r2, res2 = func(g1, c1)
      self.assertEqual(r1.numpy(), 15.0)
      self.assertRegex(r1.backing_device, 'CPU')
      self.assertEqual(r2.numpy(), 14.0)
      self.assertRegex(r2.backing_device, 'GPU')
      check_handle(res1, 3.0)
      check_handle(res2, 2.0)
  
    @test_util.run_gpu_only
    def testPassResourceThroughNestedFunctionCall(self):
      """Test passing GPU resource to noinline function call placed on CPU.
  
      PartitionedCallOp must not enforce any particular device assignment for the
      resource output. Inner function marked as `_nospecialize`, so Grappler would
      not prune unused function output.
      """
  
      with ops.device('/device:GPU:0'):
        g1 = resource_variable_ops.ResourceVariable(3.0)
  
      @function.defun_with_attributes(attributes={
          '_noinline': True,
          '_nospecialize': True
      })
      def inner(resource1):
        return resource1 * 2, resource1.handle
  
      @function.defun
      def outer(resource1):
        with ops.device('/device:CPU:0'):
          r1, _ = inner(resource1)
        return r1
  
      r1 = outer(g1)
  
      self.assertEqual(r1.numpy(), 6.0)
      self.assertRegex(r1.backing_device, 'CPU')
  
    @test_util.run_gpu_only
    def testReturnResourceFromNestedFunctionCall(self):
      """Test returning GPU resource from noinline function call placed on CPU.
  
      When inferring output devices for the return value, do not set a device for
      returns of DT_RESOURCE data type based on the device assignment of the node
      that produced that resource. As an example function call placed on CPU can
      return resources on GPU.
      """
  
      with ops.device('/device:GPU:0'):
        g1 = resource_variable_ops.ResourceVariable(3.0)
  
      @function.defun_with_attributes(attributes={
          '_noinline': True
      })
      def inner(resource1):
        resource1.assign_add(2.0)
        return resource1 * 2, resource1.handle
  
      @function.defun
      def outer(resource1):
        with ops.device('/device:CPU:0'):
          r1, res1 = inner(resource1)
        return r1, res1
  
      r1, res1 = outer(g1)
  
      self.assertEqual(r1.numpy(), 10.0)
      self.assertRegex(r1.backing_device, 'CPU')
  
      def check_handle(handle, expected_value):
        self.assertRegex(handle.backing_device, 'CPU')
        tensor = gen_resource_variable_ops.read_variable_op(
            handle, dtypes.float32)
        self.assertEqual(tensor.numpy(), expected_value)
  
      # Check that handles returned from functions are on CPU and an op using
      # the resource handle is correctly placed on the device backing the
      # resource.
      check_handle(res1, 5.0)
  
    @test_util.run_gpu_only
    def testComplexInputOutputDevicePattern(self):
      """Tests input/output mapping logic in partitioning."""
      with ops.device('/device:CPU:0'):
        rc0 = resource_variable_ops.ResourceVariable(2.0)
        rc1 = resource_variable_ops.ResourceVariable(3.0)
        cc0 = array_ops.identity(5.0)
        cc1 = array_ops.identity(7.0)
      with ops.device('/device:GPU:0'):
        rg0 = resource_variable_ops.ResourceVariable(11.0)
        rg1 = resource_variable_ops.ResourceVariable(13.0)
        cg0 = array_ops.identity(17.0)
        cg1 = array_ops.identity(19.0)
  
      # Make sure tensors are on expected devices.
      for tensor in [cc0, cc1]:
        self.assertRegex(tensor.backing_device, 'CPU:0')
      for tensor in [cg0, cg1]:
        self.assertRegex(tensor.backing_device, 'GPU:0')
  
      @function.defun
      def func(rc0, cc0, cg0, rc1, cg1, rg0, rg1, cc1):
        with ops.device('/device:CPU:0'):
          m1 = rc0 * cg0
        with ops.device('/device:GPU:0'):
          m2 = rg0 * cc0
  
        with ops.device('/device:CPU:0'):
          r1 = 1000.0 * m2 + rc1 * cg1
        with ops.device('/device:GPU:0'):
          r2 = 1000.0 * m1 + rg1 * cc1
  
        return r1, r2, m2, m1
  
      r1, r2, m2, m1 = func(rc0, cc0, cg0, rc1, cg1, rg0, rg1, cc1)
      self.assertRegex(m1.backing_device, 'CPU')
      self.assertRegex(r1.backing_device, 'CPU')
      self.assertRegex(m2.backing_device, 'GPU')
      self.assertRegex(r2.backing_device, 'GPU')
      self.assertEqual(m1.numpy(), 34.0)
      self.assertEqual(r1.numpy(), 55000.0 + 3.0 * 19.0)
      self.assertEqual(m2.numpy(), 55.0)
      self.assertEqual(r2.numpy(), 34000.0 + 13.0 * 7.0)
  
    @test_util.run_gpu_only
    def testArgumentPruning(self):
      """Tests functions taking unnecessary arguments."""
      with ops.device('/device:CPU:0'):
        c1 = constant_op.constant(5.0)
        c2 = constant_op.constant(7.0)
  
      with ops.device('/device:GPU:0'):
        g1 = constant_op.constant(11.0)
        g2 = constant_op.constant(13.0)
        g3 = constant_op.constant(17.0)
  
      @function.defun
      def func(g1, g2, c1, g3, c2):  # pylint: disable=unused-argument
        # arguments g1 and g2 are unused and can be pruned by grappler.
        return c1 * g3 * c2
  
      result = func(g1, g2, c1, g3, c2)
      self.assertEqual(result.numpy(), 5.0 * 7.0 * 17.0)
  
    def testNestedCallWatchedVariables(self):
  
      v = variables.Variable(4.)
  
      @def_function.function
      def f():
        return v ** 2.
  
      with backprop.GradientTape() as tape:
        f()
  
      self.assertEqual((v,), tape.watched_variables())
  
      @def_function.function
      def g():
        return f()
  
      with backprop.GradientTape() as tape:
        g()
  
      self.assertEqual((v,), tape.watched_variables())
  
      # f() can rely on the variable being read during its trace. g() checks that
      # variables from a function which knows about them are recorded on the
      # tape. h() tests that functions forward knowledge of variables to callers.
  
      @def_function.function
      def h():
        return g()
  
      with backprop.GradientTape() as tape:
        h()
  
      self.assertEqual((v,), tape.watched_variables())
  
    def testDeferredCapture(self):
      value = 1.0
  
      @def_function.function
      def lazy_capture(x):
        y = ops.get_default_graph().capture_call_time_value(
            lambda: value, tensor_spec.TensorSpec(None))
        return x + y
  
      self.assertAllEqual(lazy_capture(2.0), 3.0)
      # After changing the value of `value` the function call should return a
      # different result.
      value = 2.0
      self.assertAllEqual(lazy_capture(2.0), 4.0)
  
    def testNestedDeferredCapture(self):
      value = 1.0
  
      @def_function.function
      def inner(x):
        y = ops.get_default_graph().capture_call_time_value(
            lambda: value, tensor_spec.TensorSpec(None))
        return x + y
  
      @def_function.function
      def outer(x):
        return inner(x)
  
      self.assertAllEqual(outer(2.0), 3.0)
      # After changing the value of `value` the function call should return a
      # different result.
      value = 2.0
      self.assertAllEqual(outer(2.0), 4.0)
  
    def testNestedDeferredCaptureInTFWhileLoop(self):
  
      value = 1.
  
      @def_function.function
      def inner(x):
        y = ops.get_default_graph().capture_call_time_value(
            lambda: value, tensor_spec.TensorSpec(None))
        return x + y
  
      @def_function.function
      def outer():
        dummy = constant_op.constant(True)
        sums = constant_op.constant(0.)
        while dummy:
          directives.set_loop_options(
              shape_invariants=[(sums, tensor_shape.TensorShape(None))])
          sums += inner(2.)
          dummy = constant_op.constant(False)
        return sums
  
      self.assertAllEqual(outer(), 3.)
  
      value = constant_op.constant(2.)
      self.assertAllEqual(outer(), 4.)
  
      value = constant_op.constant(3.)
      self.assertAllEqual(outer(), 5.)
  
    def testDeferredCaptureWithKey(self):
      value0 = 1.0
      value1 = 2.0
  
      @def_function.function
      def lazy_capture(x):
        w = ops.get_default_graph().capture_call_time_value(
            lambda: value0, tensor_spec.TensorSpec(None), key=0)
        y = ops.get_default_graph().capture_call_time_value(
            lambda: value1, tensor_spec.TensorSpec(None), key=1)
        def bad_closure():
          raise ValueError('Should not run')
        z = ops.get_default_graph().capture_call_time_value(
            bad_closure, tensor_spec.TensorSpec(None), key=1)
        return x + y + w + z
  
      self.assertAllEqual(lazy_capture(2.0), 7.0)
      value0 = 2.0
      value1 = 3.0
      self.assertAllEqual(lazy_capture(2.0), 10.0)
  
    def testDeferredCaptureTypeError(self):
      value = constant_op.constant(1.0)
  
      @def_function.function
      def lazy_capture(x):
        y = ops.get_default_graph().capture_call_time_value(
            lambda: value, tensor_spec.TensorSpec(()))
        return x + y
  
      self.assertAllEqual(lazy_capture(2.0), 3.0)
  
      # dtype mismatch
      value = constant_op.constant(1)
      with self.assertRaisesRegex(ValueError, 'Value .* to a tensor with dtype'):
        lazy_capture(2.0)
  
      # shape mismatch
      value = constant_op.constant([1.0])
      with self.assertRaisesRegex(ValueError, 'Value .* shape'):
        lazy_capture(2.0)
  
    def testDeferredCaptureReturnNestWithCompositeTensor(self):
      i_s = indexed_slices.IndexedSlices(
          constant_op.constant([1, 2]),
          constant_op.constant([0, 1], dtype=dtypes.int64),
          constant_op.constant([2]))
      r_t = ragged_factory_ops.constant([[[1, 2], [3]], [[4, 5, 6]]])
      s_t = sparse_tensor.SparseTensor(
          values=[1, 2, 3], indices=[[0], [8], [10]], dense_shape=[20])
  
      @def_function.function
      def lazy_capture():
        y = ops.get_default_graph().capture_call_time_value(
            lambda: {'i': i_s, 't': (r_t, s_t)},
            {'i': indexed_slices.IndexedSlicesSpec(
                dtype=dtypes.int32, dense_shape_dtype=dtypes.int32),
             't': (ragged_tensor.RaggedTensorSpec([2, None, None], dtypes.int32),
                   sparse_tensor.SparseTensorSpec([None], dtypes.int32))})
        return y['i'], y['t']
  
      i, (r, s) = lazy_capture()
      self.assertAllEqual(i_s.values, i.values)
      self.assertAllEqual(i_s.indices, i.indices)
      self.assertAllEqual(i_s.dense_shape, i.dense_shape)
      self.assertAllEqual(r_t, r)
      self.assertAllEqual(s_t.indices, s.indices)
      self.assertAllEqual(s_t.values, s.values)
      self.assertAllEqual(s_t.dense_shape, s.dense_shape)
  
    def testDeferredCaptureCompositeTensorSpecTypeMismatch(self):
      value = indexed_slices.IndexedSlices(
          constant_op.constant([1, 2]),
          constant_op.constant([0, 1], dtype=dtypes.int64))
  
      @def_function.function
      def lazy_capture():
        return ops.get_default_graph().capture_call_time_value(
            lambda: value,
            indexed_slices.IndexedSlicesSpec(dtype=dtypes.int32))
  
      # Type matches spec.
      lazy_capture()
  
      # Extra dense shape component.
      value = indexed_slices.IndexedSlices(
          constant_op.constant([1, 2]),
          constant_op.constant([0, 1], dtype=dtypes.int64),
          constant_op.constant([2]))
      with self.assertRaises(ValueError):
        lazy_capture()
  
      # Index dtype mismatch int32 vs. int64.
      value = indexed_slices.IndexedSlices(
          constant_op.constant([1, 2]),
          constant_op.constant([0, 1]))
      with self.assertRaises(ValueError):
        lazy_capture()
  
    def testFunctoolsLruCache(self):
      self.skipTest(
          "b/194845243: inspect.getfullargspec doesn't unwrap Python decorators.")
  
      @def_function.function
      @functools.lru_cache(maxsize=2)
      def f(a):
        return 2 * a
  
      self.assertAllEqual(f(1), array_ops.constant(2))
  
  if __name__ == '__main__':
    ops.enable_eager_execution()
    test.main()
  