class_docstr: Statistics and time average pooling (TAP) layer
  
  This computes mean and, optionally, standard deviation statistics across the time dimension.
  
  Args:
      feat_in: Input features with shape [B, D, T]
      pool_mode: Type of pool mode. Supported modes are 'xvector' (mean and standard deviation) and 'tap' (time
          average pooling, i.e., mean)
      eps: Epsilon, minimum value before taking the square root, when using 'xvector' mode.
      biased: Whether to use the biased estimator for the standard deviation when using 'xvector' mode. The default
          for torch.Tensor.std() is True.
  
  Returns:
      Pooled statistics with shape [B, D].
  
  Raises:
      ValueError if an unsupported pooling mode is specified.
class_docstr_tok: ["Statistics","and","time","average","pooling","(","TAP",")","layer","This","computes","mean","and",",","optionally",",","standard","deviation","statistics","across","the","time","dimension",".","Args",":","feat_in",":","Input","features","with","shape","[","B",",","D",",","T","]","pool_mode",":","Type","of","pool","mode",".","Supported","modes","are","'xvector","'","(","mean","and","standard","deviation",")","and","'tap","'","(","time","average","pooling",",","i.e.",",","mean",")","eps",":","Epsilon",",","minimum","value","before","taking","the","square","root",",","when","using","'xvector","'","mode",".","biased",":","Whether","to","use","the","biased","estimator","for","the","standard","deviation","when","using","'xvector","'","mode",".","The","default","for","torch.Tensor.std","(",")","is","True",".","Returns",":","Pooled","statistics","with","shape","[","B",",","D","]",".","Raises",":","ValueError","if","an","unsupported","pooling","mode","is","specified","."]
code: def __init__(
      self,
      feat_in: int,
      pool_mode: str = "xvector",
      eps: float = 1e-10,
      biased: bool = True,
  ):
      super().__init__()
      supported_modes = {"xvector", "tap"}
      if pool_mode not in supported_modes:
          raise ValueError(
              f"Pool mode must be one of {supported_modes}; got '{pool_mode}'"
          )
      self.pool_mode = pool_mode
      self.feat_in = feat_in
      self.eps = eps
      self.biased = biased
      if self.pool_mode == "xvector":
          # Mean + std
          self.feat_in *= 2
  
imports: from typing import List
  import torch
  from numpy import inf
  from torch import nn
  from torch.nn import functional
  from nemo.collections.asr.parts.submodules.jasper import get_same_padding, init_weights
label_desc: Use image node_id 1 to create a new StatsPoolLayer object from inherited base classes: nn with example: obj = StatsPoolLayer(feat_in, pool_mode, eps, biased)