class_docstr: Categorical distribution with noise for discrete action spaces
class_docstr_tok: ["Categorical","distribution","with","noise","for","discrete","action","spaces"]
docstr: Args:
      logits: A float32 tensor with shape [BATCH_SIZE, NUM_ACTIONS] of unnormalized policy logits
docstr_tok: ["Args",":","logits",":","A","float32","tensor","with","shape","[","BATCH_SIZE",",","NUM_ACTIONS","]","of","unnormalized","policy","logits"]
code: def __init__(self, logits):
      """
      Args:
          logits: A float32 tensor with shape [BATCH_SIZE, NUM_ACTIONS] of unnormalized policy logits
      """
      self.logits = logits
      super(SoftCategoricalDistribution, self).__init__(logits)
  
imports: import torch
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 1 to create a new SoftCategoricalDistribution object from inherited base classes: CategoricalDistribution with example: obj = SoftCategoricalDistribution(logits)