class_docstr: An implementation of residual addition of an adapter module with its input.
  Supports stochastic depth regularization.
class_docstr_tok: ["An","implementation","of","residual","addition","of","an","adapter","module","with","its","input",".","Supports","stochastic","depth","regularization","."]
docstr: An implementation of residual addition of an adapter module with its input.
  Performs output = input + adapter(input).
  
  Args:
      stochastic_depth: float, when greater than one, can optionally dropout the output of
          the adapter's forward pass.
      l2_lambda: L2 norm of the difference between the original input to the function, and the adapter's
          output result. Disabled if set to 0.0.
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code: def __init__(
      self, stochastic_depth: float = 0.0, l2_lambda: float = 0.0
  ):
      """
      An implementation of residual addition of an adapter module with its input.
      Performs output = input + adapter(input).
  
      Args:
          stochastic_depth: float, when greater than one, can optionally dropout the output of
              the adapter's forward pass.
          l2_lambda: L2 norm of the difference between the original input to the function, and the adapter's
              output result. Disabled if set to 0.0.
      """
      super().__init__()
      self.stochastic_depth = stochastic_depth
      self.l2_lambda = l2_lambda
  
imports: from abc import ABC
  from dataclasses import dataclass
  from typing import Any, Dict, List, Tuple, Union
  import torch
  from nemo.core.classes.mixins import AccessMixin
label_desc: Use image node_id 1 to create a new ResidualAddAdapterStrategy object from inherited base classes: AbstractAdapterStrategy with example: obj = ResidualAddAdapterStrategy(stochastic_depth, l2_lambda)