docstr: Generates features and length output from the given input tensor.
  
  Args:
      input (torch.Tensor): input tensor.
  
  Returns:
      (torch.Tensor, torch.Tensor):
      torch.Tensor:
          Features, with shape `(length, *)`.
      torch.Tensor:
          Length, with shape `(1,)`.
docstr_tok: ["Generates","features","and","length","output","from","the","given","input","tensor",".","Args",":","input","(","torch.Tensor",")",":","input","tensor",".","Returns",":","(","torch.Tensor",",","torch.Tensor",")",":","torch.Tensor",":","Features",",","with","shape","`","(","length",",","*",")","`",".","torch.Tensor",":","Length",",","with","shape","`","(","1",",",")","`","."]
code: def __call__(
      self, input: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Generates features and length output from the given input tensor.
  
      Args:
          input (torch.Tensor): input tensor.
  
      Returns:
          (torch.Tensor, torch.Tensor):
          torch.Tensor:
              Features, with shape `(length, *)`.
          torch.Tensor:
              Length, with shape `(1,)`.
      """
  
imports: import json
  import math
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from functools import partial
  from typing import Callable, List, Tuple
  import torch
  import torchaudio
  from torchaudio._internal import module_utils
  from torchaudio.models import emformer_rnnt_base, RNNT, RNNTBeamSearch
label_desc: Use image node_id 1 for calling the _FeatureExtractor obj's underlying member method code with example usage: obj.__call__(input) without return types