class_docstr: GRN (Global Response Normalization) layer
class_docstr_tok: ["GRN","(","Global","Response","Normalization",")","layer"]
code: def __init__(self, config: ConvNextV2Config, dim: int, **kwargs):
      super().__init__(**kwargs)
      self.dim = dim
  
imports: from __future__ import annotations
  from typing import List, Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPooling, TFBaseModelOutputWithPoolingAndNoAttention, TFImageClassifierOutputWithNoAttention
  from ...modeling_tf_utils import TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_convnextv2 import ConvNextV2Config
label_desc: Use image node_id 1 to create a new TFConvNextV2GRN object from inherited base classes: tf.keras.layers with example: obj = TFConvNextV2GRN(config, dim)