code: def get_features(self, x: torch.Tensor) -> torch.Tensor:
      x = x.view(1, 1, -1)
      size = x.size(2)
      feat = []
      start = 0
      while start < size:
          if start + self.max_chunk > size:
              break
          x_chunk = x[..., start : start + self.max_chunk]
          feat_chunk = self.model.extract_features(
              source=x_chunk,
              get_encoded=self.use_encoder_layer,
              norm_output=self.norm_features,
          )
          feat.append(feat_chunk)
          start += self.max_chunk
  
      if start < size:
          x_chunk = x[:, -self.max_chunk :]
          feat_chunk = self.model.extract_features(
              source=x_chunk,
              get_encoded=self.use_encoder_layer,
              norm_output=self.norm_features,
          )
          df = x_chunk.size(2) // feat_chunk.size(1)
          delta = (size - start) // df
          feat.append(feat_chunk[:, -delta:])
      return torch.cat(feat, 1).squeeze(0)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the CpcFeatureReader obj's underlying member method code with example usage: obj.get_features(x) and returns: torch