code: def __init__(self, in_dim, out_dim, bias=True, w_init_gain="linear"):
      super(LinearNorm, self).__init__()
      self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
  
      torch.nn.init.xavier_uniform_(
          self.linear_layer.weight,
          gain=torch.nn.init.calculate_gain(w_init_gain),
      )
  
imports: import torch
  from librosa.filters import mel
  from .audio_processing import dynamic_range_compression
  from .audio_processing import dynamic_range_decompression
  from .stft import STFT
  from .utils import get_mask_from_lengths
label_desc: Use image node_id 1 to create a new LinearNorm object from inherited base classes: torch.nn with example: obj = LinearNorm(in_dim, out_dim, bias, w_init_gain)