class_docstr: This class transforms a float32 tensor into a lower resolution one
  (e.g., int16, int8, float16) and then converts it back to a float32.
  This process loses information and can be used for data augmentation.
  
  Arguments:
  ---------
      target_dtype: str
          One of "int16", "int8", "float16". If "random", the bit resolution
          is randomly selected among the options listed above.
  
  Example:
      >>> dropper = DropBitResolution()
      >>> signal = torch.rand(4, 16000)
      >>> signal_dropped = dropper(signal)
class_docstr_tok: ["This","class","transforms","a","float32","tensor","into","a","lower","resolution","one","(","e.g.",",","int16",",","int8",",","float16",")","and","then","converts","it","back","to","a","float32",".","This","process","loses","information","and","can","be","used","for","data","augmentation",".","Arguments",":","--","--","--","--","-","target_dtype",":","str","One","of","``","int16","''",",","``","int8","''",",","``","float16","''",".","If","``","random","''",",","the","bit","resolution","is","randomly","selected","among","the","options","listed","above",".","Example",":",">",">",">","dropper","=","DropBitResolution","(",")",">",">",">","signal","=","torch.rand","(","4",",","16000",")",">",">",">","signal_dropped","=","dropper","(","signal",")"]
code: def __init__(self, target_dtype="random"):
      super().__init__()
  
      self.target_dtype = target_dtype
      self.bit_depths = {
          "int16": (16, torch.int16),
          "int8": (8, torch.int8),
          "float16": (16, torch.float16),
      }
  
      if (
          self.target_dtype != "random"
          and self.target_dtype not in self.bit_depths
      ):
          raise ValueError(
              f"target_dtype must be one of {list(self.bit_depths.keys())}"
          )
  
imports: import math
  import random
  import torch
  import torch.nn.functional
  from speechbrain.dataio.legacy import ExtendedCSVDataset
  from speechbrain.dataio.dataloader import make_dataloader
  from speechbrain.processing.signal_processing import compute_amplitude, dB_to_amplitude, convolve1d, notch_filter, reverberate
label_desc: Use image node_id 1 to create a new DropBitResolution object from inherited base classes: torch.nn with example: obj = DropBitResolution(target_dtype)