class_docstr: GRN (Global Response Normalization) layer
class_docstr_tok: ["GRN","(","Global","Response","Normalization",")","layer"]
code: def call(self, hidden_states: tf.Tensor):
      global_features = tf.norm(
          hidden_states, ord="euclidean", axis=(1, 2), keepdims=True
      )
      norm_features = global_features / (
          tf.reduce_mean(global_features, axis=-1, keepdims=True) + 1e-6
      )
      hidden_states = (
          self.weight * (hidden_states * norm_features)
          + self.bias
          + hidden_states
      )
      return hidden_states
  
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 3 for calling the TFConvNextV2GRN obj's underlying member method code with example usage: obj.call(hidden_states) and returns: hidden_states