class_docstr: A modified version of the `BertLayer` class from the
  `transformers.models.bert.modeling_bert` module.
  
  Args:
      config (:class:`~transformers.BertConfig`):
          The configuration object that
          contains various parameters for the model.
      clamp_min_for_underflow (bool, optional):
          Whether to clamp the minimum value of the hidden states
           to prevent underflow. Defaults to `False`.
      clamp_max_for_overflow (bool, optional):
          Whether to clamp the maximum value of the hidden states
          to prevent overflow. Defaults to `False`.
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docstr: Applies the intermediate and output layers of the BertEncoderLayer
  to a chunk of the input sequence.
docstr_tok: ["Applies","the","intermediate","and","output","layers","of","the","BertEncoderLayer","to","a","chunk","of","the","input","sequence","."]
code: def feed_forward_chunk(self, attention_output: Tensor) -> Tensor:
      """Applies the intermediate and output layers of the BertEncoderLayer
      to a chunk of the input sequence."""
      intermediate_output = self.intermediate(attention_output)
      layer_output = self.output(intermediate_output, attention_output)
      return layer_output
  
imports: import math
  from typing import Dict, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn.bricks import DropPath
  from torch import Tensor
label_desc: Use image node_id 3 for calling the BertEncoderLayer obj's underlying member method code with example usage: obj.feed_forward_chunk(attention_output) and returns: layer_output