class_docstr: Aggregates frame-level features to single utterance-level feature.
  Proposed in B.Desplanques et al., "ECAPA-TDNN: Emphasized Channel
  Attention, Propagation and Aggregation in TDNN Based Speaker Verification"
  
  args:
      input_size: dimensionality of the input frame-level embeddings.
          Determined by encoder hyperparameter.
          For this pooling layer, the output dimensionality will be double of
          the input_size
code: def output_size(self):
      return self._output_size
  
imports: import torch
  import torch.nn
  from espnet2.spk.pooling.abs_pooling import AbsPooling
label_desc: Use image node_id 2 for calling the ChnAttnStatPooling obj's underlying member method code with example usage: obj.output_size() and returns: self