class_docstr: Builds Approximation instance from a given trace,
  it has the same interface as variational approximation
class_docstr_tok: ["Builds","Approximation","instance","from","a","given","trace",",","it","has","the","same","interface","as","variational","approximation"]
code: def randidx(self, size=None):
      if size is None:
          size = (1,)
      elif isinstance(size, TensorVariable):
          if size.ndim < 1:
              size = size[None]
          elif size.ndim > 1:
              raise ValueError("size ndim should be no more than 1d")
          else:
              pass
      else:
          size = tuple(np.atleast_1d(size))
      return pt.random.integers(
          size=size,
          low=0,
          high=self.histogram.shape[0],
          rng=pytensor.shared(np.random.default_rng()),
      )
  
imports: from typing import Optional
  import numpy
  import pytensor
  from arviz import InferenceData
  from pytensor import tensor
  from pytensor.graph.basic import Variable
  from pytensor.graph.replace import graph_replace
  from pytensor.tensor.variable import TensorVariable
  import pymc
  from pymc.blocking import DictToArrayBijection
  from pymc.distributions.dist_math import rho2sigma
  from pymc.util import makeiter
  from pymc.variational import opvi
  from pymc.variational.opvi import Approximation, Group, NotImplementedInference, _known_scan_ignored_inputs, node_property
label_desc: Use image node_id 4 for calling the EmpiricalGroup obj's underlying member method code with example usage: obj.randidx(size) and returns: pt