class_docstr: Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
  
  This module is the same as a ``torch.nn.Linear``` layer, except that in the backward pass
  the grad_samples get accumulated (instead of being concatenated as in the standard
  nn.Linear).
  
  When used with `PackedSequence`s, additional attribute `max_batch_len` is defined to determine
  the size of per-sample grad tensor.
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code: def __init__(
      self, in_features: int, out_features: int, bias: bool = True
  ):
      super().__init__(in_features, out_features, bias)
  
imports: import math
  import numbers
  import warnings
  from typing import List, Optional, Tuple, Type, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn.utils.rnn import PackedSequence
  from ..utils.packed_sequences import compute_seq_lengths
  from .param_rename import RenameParamsMixin
label_desc: Use image node_id 1 to create a new RNNLinear object from inherited base classes: nn with example: obj = RNNLinear(in_features, out_features, bias)