class_docstr: Converts samples from a uniform into sample from a multinomial.
class_docstr_tok: ["Converts","samples","from","a","uniform","into","sample","from","a","multinomial","."]
code: def make_node(self, pvals, unis, n=1):
      pvals = T.as_tensor_variable(pvals)
      unis = T.as_tensor_variable(unis)
      if pvals.ndim != 2:
          raise NotImplementedError(
              "pvals ndim should be 2", pvals.ndim
          )
      if unis.ndim != 1:
          raise NotImplementedError("unis ndim should be 1", unis.ndim)
      if self.odtype == "auto":
          odtype = pvals.dtype
      else:
          odtype = self.odtype
      out = T.tensor(
          dtype=odtype, broadcastable=pvals.type.broadcastable
      )
      return Apply(self, [pvals, unis, as_scalar(n)], [out])
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import warnings
  import theano
  from theano import Op, Apply
  import theano.tensor
  from theano.scalar import as_scalar
  import copy
label_desc: Use image node_id 4 for calling the MultinomialFromUniform obj's underlying member method code with example usage: obj.make_node(pvals, unis, n) and returns: Apply