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)

        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
        
EVA-02/det/projects/DensePose/densepose/data/samplers/densepose_uniform.py