class_docstr: Compute attention based on dot product.
  
  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.
class_docstr_tok: ["Compute","attention","based","on","dot","product",".","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","."]
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 AttDot obj's underlying member method code with example usage: obj.reset() without return types