std::vector<Operation*> work_list_;
  absl::flat_hash_set<Operation*> quantized_;

  // The vector contains all the quantization parameters propagated from the
  // defining operations of the value, or from the quantization aware training.
  std::vector<QuantState> states_;

  // The map contains all the quantization parameters which are required to
  // satisfy the same operands and results constraint. The keys of this map are

sschepens <******@****.***> 1717506007 -0300

  name: "training/LossScaleOptimizer/SGD/update_95/ResourceApplyKerasMomentum/ReadVariableOp_1"
  op: "ReadVariableOp"
  input: "training_lossscaleoptimizer_sgd_update_resourceapplykerasmomentum_readvariableop_1_resource"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
}
node {

    }
  }
  attr {
    key: "shape"
    value {
      shape {
      }
    }
  }
  attr {
    key: "shared_name"
    value {
      s: "training/SGD/iter"
    }
  }
}
node {
  name: "training/SGD/bn2a_branch1/beta/momentum"
  op: "VarHandleOp"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "container"
    value {
      s: ""

  });
  return failure(walk_result.wasInterrupted());
}

// Updates the operand of TPU embedding enqueue ops depending on whether
// the graph is in training mode or in non-training mode.
// If SendTPUEmbeddingGradients op is present, this means that graph is in
// training mode. As so, correctly feed in `then` branch value of SelectV2
// operand as inputs to the TPU embedding enqueue ops.
LogicalResult UpdateEmbeddingEnqueueOpInput(

  public void testIsPresent_yes() {
    assertTrue(Optional.of("training").isPresent());
  }

  public void testGet_absent() {
    Optional<String> optional = Optional.absent();
    try {
      optional.get();
      fail();
    } catch (IllegalStateException expected) {
    }
  }

  public void testGet_present() {
    assertEquals("training", Optional.of("training").get());
  }

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// https://github.com/tensorflow/tensorflow/blob/fda326e542ca67534e8411edb180e8760a4828b7/tensorflow/python/training/tracking/tracking.py#L285
// and are expected to implement "_create_resource", "_initialize", and
// "_destroy_resource" functions:
// https://github.com/tensorflow/tensorflow/blob/139ba9c5284799beafdd1d7f895127cf00e7c48f/tensorflow/python/training/tracking/tracking.py#L262-L281
class RestoredResource : TensorHandleConvertible {
 public:

  // Currently we don't use core_selector for training use cases.
  bool tpu_use_core_selector = false;

  // If true, use BundledTransferToTpuOp to transfer variables and input tensors
  // to TPU.
  bool tpu_use_bundled_transfer = false;

  // If true, lower an TF op that's placed on TPU device to be executed with
  // tfrt_fallback.execute.
  // Currently for training use cases we need to lower the op to corert.execute

// Applies post-training dynamic-range quantization to the model.
absl::StatusOr<ExportedModel> QuantizeDynamicRangePtq(
    absl::string_view saved_model_path,
    const std::vector<std::string>& signature_keys,
    const std::unordered_set<std::string>& tags,
    const QuantizationOptions& quantization_options);

// Applies post-training static-range weight-only quantization to the model.