code: def forward(
      self,
      x: ComplexTensor,
      ilens: Union[torch.LongTensor, np.ndarray, List[int]],
  ) -> Tuple[torch.Tensor, torch.LongTensor]:
      # (B, T, F) or (B, T, C, F)
      if x.dim() not in (3, 4):
          raise ValueError(f"Input dim must be 3 or 4: {x.dim()}")
      if not torch.is_tensor(ilens):
          ilens = torch.from_numpy(np.asarray(ilens)).to(x.device)
  
      if x.dim() == 4:
          # h: (B, T, C, F) -> h: (B, T, F)
          if self.training:
              # Select 1ch randomly
              ch = np.random.randint(x.size(2))
              h = x[:, :, ch, :]
          else:
              # Use the first channel
              h = x[:, :, 0, :]
      else:
          h = x
  
      # h: ComplexTensor(B, T, F) -> torch.Tensor(B, T, F)
      h = h.real**2 + h.imag**2
  
      h, _ = self.logmel(h, ilens)
      if self.stats_file is not None:
          h, _ = self.global_mvn(h, ilens)
      if self.apply_uttmvn:
          h, _ = self.uttmvn(h, ilens)
  
      return h, ilens
  
imports: from typing import List, Tuple, Union
  import librosa
  import numpy
  import torch
  from torch_complex.tensor import ComplexTensor
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
label_desc: Use image node_id 2 for calling the FeatureTransform obj's underlying member method code with example usage: obj.forward(x, ilens) and returns: h,  ilens