class_docstr: Progressive Layer Dropping (PLD) for model training.
      This implements the PLD technique for compressed model training
      from this paper: https://arxiv.org/pdf/2010.13369.pdf
  Args:
      theta (float): a hyper-parameter that controls the trade-off between training time and robustness.
      The lower the theta value, the faster the training speed. Default value: 0.5.
      gamma (float): a hyper-parameter that controls how fast the drop ratio increases. Default value: 0.001.
code: def get_theta(self):
      return self.current_theta
  
file_path: deepspeed/deepspeed/runtime/progressive_layer_drop.py
imports: import numpy
  from deepspeed.utils import log_dist
label_desc: Use image node_id 3 for calling the ProgressiveLayerDrop obj's underlying member method code with example usage: obj.get_theta() and returns: self