class_docstr: Elementwise affine flow module.
class_docstr_tok: ["Elementwise","affine","flow","module","."]
docstr: Initialize ElementwiseAffineFlow module.
  
  Args:
      channels (int): Number of channels.
docstr_tok: ["Initialize","ElementwiseAffineFlow","module",".","Args",":","channels","(","int",")",":","Number","of","channels","."]
code: def __init__(self, channels: int):
      """Initialize ElementwiseAffineFlow module.
  
      Args:
          channels (int): Number of channels.
  
      """
      super().__init__()
      self.channels = channels
      self.register_parameter(
          "m", torch.nn.Parameter(torch.zeros(channels, 1))
      )
      self.register_parameter(
          "logs", torch.nn.Parameter(torch.zeros(channels, 1))
      )
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  from espnet2.gan_tts.vits.transform import piecewise_rational_quadratic_transform
label_desc: Use image node_id 1 to create a new ElementwiseAffineFlow object from inherited base classes: torch.nn with example: obj = ElementwiseAffineFlow(channels)