class_docstr: Samples DensePose data from DensePose predictions.
  Samples for each class are drawn uniformly over all pixels estimated
  to belong to that class.
docstr: Produce a uniform sample of indices to select data
  
  Args:
      values (torch.Tensor): an array of size [n, k] that contains
          estimated values (U, V, confidences);
          n: number of channels (U, V, confidences)
          k: number of points labeled with part_id
      count (int): number of samples to produce, should be positive and <= k
  
  Return:
      list(int): indices of values (along axis 1) selected as a sample
docstr_tok: ["Produce","a","uniform","sample","of","indices","to","select","data","Args",":","values","(","torch.Tensor",")",":","an","array","of","size","[","n",",","k","]","that","contains","estimated","values","(","U",",","V",",","confidences",")",";","n",":","number","of","channels","(","U",",","V",",","confidences",")","k",":","number","of","points","labeled","with","part_id","count","(","int",")",":","number","of","samples","to","produce",",","should","be","positive","and","<","=","k","Return",":","list","(","int",")",":","indices","of","values","(","along","axis","1",")","selected","as","a","sample"]
code: def _produce_index_sample(self, values: torch.Tensor, count: int):
      """
      Produce a uniform sample of indices to select data
  
      Args:
          values (torch.Tensor): an array of size [n, k] that contains
              estimated values (U, V, confidences);
              n: number of channels (U, V, confidences)
              k: number of points labeled with part_id
          count (int): number of samples to produce, should be positive and <= k
  
      Return:
          list(int): indices of values (along axis 1) selected as a sample
      """
      k = values.shape[1]
      return random.sample(range(k), count)
  
imports: import random
  import torch
  from .densepose_base import DensePoseBaseSampler
label_desc: Use image node_id 2 for calling the DensePoseUniformSampler obj's underlying member method code with example usage: obj._produce_index_sample(values, count) and returns: random