class_docstr: Base class for memories organized as a queue / circular buffer.
  
  Args:
      capacity (int > 0): Memory capacity
          (<span style="color:#00C000"><b>default</b></span>: minimum capacity).
      device (string): Device name
          (<span style="color:#00C000"><b>default</b></span>: CPU:0).
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      values_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      min_capacity (int >= 0): <span style="color:#0000C0"><b>internal use</b></span>.
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code: def retrieve(self, *, indices, values):
      assert isinstance(values, tuple)
      function = lambda x: tf.gather(params=x, indices=indices)
      return self.buffers[values].fmap(
          function=function, cls=TensorDict
      )
  
file_path: tensorforce/tensorforce/core/memories/queue.py
imports: import numpy
  import tensorflow
  from tensorforce import TensorforceError, util
  from tensorforce.core import TensorDict, TensorSpec, tf_function, tf_util, VariableDict
  from tensorforce.core.memories import Memory
label_desc: Use image node_id 5 for calling the Queue obj's underlying member method code with example usage: obj.retrieve() and returns: self