code: def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
      super().__init__()
      self.weight = torch.nn.Parameter(torch.randn(out_dim, in_dim))
      if bias:
          self.bias = torch.nn.Parameter(torch.zeros(out_dim))
  
      self.lr_mul = lr_mul
  
imports: import math
  from functools import partial
  from math import log2
  from typing import List
  import torch
  import torch.nn.functional
  from einops import rearrange
  from kornia.filters import filter2d
  from nemo.collections.tts.parts.utils.helpers import mask_sequence_tensor
label_desc: Use image node_id 1 to create a new EqualLinear object from inherited base classes: torch.nn with example: obj = EqualLinear(in_dim, out_dim, lr_mul, bias)