class_docstr: A variable which packs multiple variables distributed across devices.
  
  It's only supported when eager execution is enabled.
  For op-by-op execution, use an unpacked handle on the current device; for
  function execution, use the packed handle to reduce the overhead of function
  calls.
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code: def is_initialized(self, name=None):
      if context.executing_eagerly():
          result = self._distributed_variables[0].is_initialized()
          for v in self._distributed_variables[1:-1]:
              result = math_ops.logical_and(result, v.is_initialized())
          result = math_ops.logical_and(
              result,
              self._distributed_variables[-1].is_initialized(),
              name=name,
          )
      else:
          with ops.device(self._devices[0]):
              result = super(
                  PackedDistributedVariable, self
              ).is_initialized(name)
          for d in self._devices[1:-1]:
              with ops.device(d):
                  initialized = super(
                      PackedDistributedVariable, self
                  ).is_initialized(name)
              result = math_ops.logical_and(result, initialized)
          with ops.device(self._devices[-1]):
              initialized = super(
                  PackedDistributedVariable, self
              ).is_initialized(name)
          result = math_ops.logical_and(result, initialized, name=name)
      return result
  
imports: from tensorflow.python.distribute import device_util
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion_registry
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
label_desc: Use image node_id 11 for calling the PackedDistributedVariable obj's underlying member method code with example usage: obj.is_initialized(name) and returns: result