class_docstr: The soft-max multi-class likelihood.  It can only provide a stochastic
  Monte-Carlo estimate of the variational expectations term, but this
  added variance tends to be small compared to that due to mini-batching
  (when using the SVGP model).
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code: def _log_prob(
      self, X: TensorType, F: TensorType, Y: TensorType
  ) -> tf.Tensor:
      return -tf.nn.sparse_softmax_cross_entropy_with_logits(
          logits=F, labels=Y[:, 0]
      )
  
imports: from typing import Any, Optional
  import numpy
  import tensorflow
  import tensorflow_probability
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import MeanAndVariance, Module, Parameter, TensorType
  from ..config import default_float
  from ..quadrature import hermgauss
  from ..utilities import to_default_float, to_default_int
  from .base import Likelihood, MonteCarloLikelihood
label_desc: Use image node_id 2 for calling the Softmax obj's underlying member method code with example usage: obj._log_prob(X, F, Y) and returns: unknown