class_docstr: Conformer Encoder class, consisting of feature projection and conformer modules.
  
  Args:
      feature_projection (nn.Module):
          Projects feature to encoder dimension.
      conformer (nn.ModuleList)
          List of Conformer layers.
code: def _get_intermediate_outputs(
      self,
      x: Tensor,
      mask: Optional[Tensor] = None,
      num_layers: Optional[int] = None,
  ) -> List[Tensor]:
      if num_layers is not None:
          if not 0 < num_layers <= len(self.conformer):
              raise ValueError(
                  f"`num_layers` must be between [1, {len(self.conformer)}]"
              )
  
      ret: List[Tensor] = []
  
      x = x.transpose(0, 1)
      for layer in self.conformer:
          x = layer(x, mask)
          ret.append(x.transpose(0, 1))
          if num_layers is not None and len(ret) >= num_layers:
              return ret
      return ret
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn, Tensor
  from torch.nn import Module, ModuleList
  from torchaudio.models import Wav2Vec2Model
  from torchaudio.models.conformer import ConformerLayer
  from torchaudio.models.rnnt import _TimeReduction
  from torchaudio.models.wav2vec2 import components
label_desc: Use image node_id 3 for calling the ConformerEncoder obj's underlying member method code with example usage: obj._get_intermediate_outputs(x, mask, num_layers) and returns: ret, ret