class_docstr: Basic convolution layers including: Conv3x3, BatchNorm2d, ReLU layers.
code: def forward(self, hidden_state):
      hidden_state = self.conv(hidden_state)
      hidden_state = self.batch_norm(hidden_state)
      hidden_state = self.relu(hidden_state)
  
      return hidden_state
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  import torch
  from torch import nn
  from ...None import AutoBackbone
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from .configuration_vitmatte import VitMatteConfig
label_desc: Use image node_id 2 for calling the VitMatteBasicConv3x3 obj's underlying member method code with example usage: obj.forward(hidden_state) and returns: hidden_state