class_docstr: Compute location-based attention.
  
  Args:
      eprojs (int | None): Dimension of input vectors from encoder.
      dunits (int | None): Dimension of input vectors for decoder.
      att_dim (int): Dimension of input vectors for attention.
      aconv_chans (int): Number of channels of output arrays from convolutional layer.
      aconv_filts (int): Size of filters of convolutional layer.
class_docstr_tok: ["Compute","location-based","attention",".","Args",":","eprojs","(","int","|","None",")",":","Dimension","of","input","vectors","from","encoder",".","dunits","(","int","|","None",")",":","Dimension","of","input","vectors","for","decoder",".","att_dim","(","int",")",":","Dimension","of","input","vectors","for","attention",".","aconv_chans","(","int",")",":","Number","of","channels","of","output","arrays","from","convolutional","layer",".","aconv_filts","(","int",")",":","Size","of","filters","of","convolutional","layer","."]
docstr: Reset states.
docstr_tok: ["Reset","states","."]
code: def reset(self):
      """Reset states."""
      self.h_length = None
      self.enc_h = None
      self.pre_compute_enc_h = None
  
file_path: espnet/espnet/nets/chainer_backend/rnn/attentions.py
imports: import chainer
  import chainer.functions
  import chainer.links
  import numpy
label_desc: Use image node_id 2 for calling the AttLoc obj's underlying member method code with example usage: obj.reset() without return types