class_docstr: GRN (Global Response Normalization) layer
class_docstr_tok: ["GRN","(","Global","Response","Normalization",")","layer"]
code: def __init__(self, dim: int):
      super().__init__()
      self.weight = nn.Parameter(torch.zeros(1, 1, 1, dim))
      self.bias = nn.Parameter(torch.zeros(1, 1, 1, dim))
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BackboneOutput, BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ...utils.backbone_utils import BackboneMixin
  from .configuration_convnextv2 import ConvNextV2Config
label_desc: Use image node_id 1 to create a new ConvNextV2GRN object from inherited base classes: nn with example: obj = ConvNextV2GRN(dim)