class_docstr: Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
class_docstr_tok: ["Squeeze","and","Excitation","layer","(","SE",")","proposed","in","[","Squeeze-and-Excitation","Networks","]","(","https",":","\/\/arxiv.org\/abs\/1709.01507",")","."]
code: def call(self, hidden_state):
      # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
      pooled = self.pooler(hidden_state)
      for layer_module in self.attention:
          pooled = layer_module(pooled)
      hidden_state = hidden_state * pooled
      return hidden_state
  
imports: from typing import Optional, Tuple, Union
  import tensorflow
  from ...activations_tf import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 2 for calling the TFRegNetSELayer obj's underlying member method code with example usage: obj.call(hidden_state) and returns: hidden_state