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 update_state(self, global_step):
      def _prob(x, gamma, p):
          return (1.0 - p) * np.exp(-gamma * x) + p
  
      self.current_theta = _prob(global_step, self.gamma, self.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 4 for calling the ProgressiveLayerDrop obj's underlying member method code with example usage: obj.update_state(global_step) and returns: unknown