class_docstr: Activated Batch Normalization
  
  This gathers a BatchNorm and an activation function in a single module
  
  Parameters
  ----------
  num_features : int
      Number of feature channels in the input and output.
  eps : float
      Small constant to prevent numerical issues.
  momentum : float
      Momentum factor applied to compute running statistics.
  affine : bool
      If `True` apply learned scale and shift transformation after normalization.
  act_layer : str or nn.Module type
      Name or type of the activation functions, one of: `leaky_relu`, `elu`
  act_param : float
      Negative slope for the `leaky_relu` activation.
code: def reset_parameters(self):
      nn.init.constant_(self.running_mean, 0)
      nn.init.constant_(self.running_var, 1)
      if self.affine:
          nn.init.constant_(self.weight, 1)
          nn.init.constant_(self.bias, 0)
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the InplaceAbn obj's underlying member method code with example usage: obj.reset_parameters() without return types