class_docstr: Emission Model of the HMM, it represents the probability of
  emitting an observation based on the current state
class_docstr_tok: ["Emission","Model","of","the","HMM",",","it","represents","the","probability","of","emitting","an","observation","based","on","the","current","state"]
code: def sample(self, means, stds, sampling_temp):
      return (
          self.distribution_function(
              means, stds * sampling_temp
          ).sample()
          if sampling_temp > 0
          else means
      )
  
imports: from typing import List
  import torch
  import torch.distributions
  import torch.nn.functional
  from torch import nn
  from torch.utils.checkpoint import checkpoint
  from TTS.tts.layers.overflow.common_layers import Outputnet, OverflowUtils
  from TTS.tts.layers.tacotron.common_layers import Prenet
  from TTS.tts.utils.helpers import sequence_mask
label_desc: Use image node_id 2 for calling the EmissionModel obj's underlying member method code with example usage: obj.sample(means, stds, sampling_temp) and returns: unknown