class_docstr: RV whose logprob should be rescaled to match total_size
class_docstr_tok: ["RV","whose","logprob","should","be","rescaled","to","match","total_size"]
code: def make_node(self, rv, *total_size):
      rv = as_tensor_variable(rv)
      total_size = [
          as_tensor_variable(t, dtype="int64", ndim=0)
          if t is not None
          else NoneConst
          for t in total_size
      ]
      assert len(total_size) == rv.ndim
      out = rv.type()
      return Apply(self, [rv, *total_size], [out])
  
file_path: pymc/pymc/variational/minibatch_rv.py
imports: from typing import Any, Sequence, Union, cast
  import pytensor.tensor
  from pytensor import Variable, config
  from pytensor.graph import Apply, Op
  from pytensor.tensor import NoneConst, TensorVariable, as_tensor_variable
  from pymc.logprob.abstract import MeasurableVariable, _logprob, _logprob_helper
label_desc: Use image node_id 1 for calling the MinibatchRandomVariable obj's underlying member method code with example usage: obj.make_node(rv) and returns: Apply