code: def __init__(self, num_features, epsilon=1e-05, affine=True):
      super(ChannelNorm, self).__init__()
      if affine:
          self.weight = nn.parameter.Parameter(
              torch.Tensor(1, num_features, 1)
          )
          self.bias = nn.parameter.Parameter(
              torch.Tensor(1, num_features, 1)
          )
      else:
          self.weight = None
          self.bias = None
      self.epsilon = epsilon
      self.p = 0
      self.affine = affine
      self.reset_parameters()
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ChannelNorm object from inherited base classes: nn with example: obj = ChannelNorm(num_features, epsilon, affine)