class_docstr: Wrapper class to run inference on CPC model.
  Helps extract features for a given audio file.
class_docstr_tok: ["Wrapper","class","to","run","inference","on","CPC","model",".","Helps","extract","features","for","a","given","audio","file","."]
code: def __init__(
      self,
      checkpoint_path,
      layer,
      use_encoder_layer=False,
      norm_features=False,
      sample_rate=16000,
      max_chunk=64000,
      use_cuda=True,
  ):
      self.model = load_cpc_model(checkpoint_path, layer).eval()
      self.sample_rate = sample_rate
      self.max_chunk = max_chunk
      self.norm_features = norm_features
      self.use_encoder_layer = use_encoder_layer
      self.use_cuda = use_cuda
      if self.use_cuda:
          self.model.cuda()
  
imports: import soundfile
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new CpcFeatureReader object  with example: obj = CpcFeatureReader(checkpoint_path, layer, use_encoder_layer, norm_features, sample_rate, max_chunk, use_cuda)