class_docstr: A convolutional layer that is used with LoRA.
  
  Parameters:
      in_features (`int`):
          Number of input features.
      out_features (`int`):
          Number of output features.
      rank (`int`, `optional`, defaults to 4):
          The rank of the LoRA layer.
      kernel_size (`int` or `tuple` of two `int`, `optional`, defaults to 1):
          The kernel size of the convolution.
      stride (`int` or `tuple` of two `int`, `optional`, defaults to 1):
          The stride of the convolution.
      padding (`int` or `tuple` of two `int` or `str`, `optional`, defaults to 0):
          The padding of the convolution.
      network_alpha (`float`, `optional`, defaults to `None`):
          The value of the network alpha used for stable learning and preventing underflow. This value has the same
          meaning as the `--network_alpha` option in the kohya-ss trainer script. See
          https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
code: def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
      orig_dtype = hidden_states.dtype
      dtype = self.down.weight.dtype
  
      down_hidden_states = self.down(hidden_states.to(dtype))
      up_hidden_states = self.up(down_hidden_states)
  
      if self.network_alpha is not None:
          up_hidden_states *= self.network_alpha / self.rank
  
      return up_hidden_states.to(orig_dtype)
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from ..utils import logging
  from ..utils.import_utils import is_transformers_available
label_desc: Use image node_id 2 for calling the LoRAConv2dLayer obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: up_hidden_states