original_docstring: 
          Input: p - list of floats [0-1] representing action probability distrabution
          Description: add dirichlet noise to probability distrabution
          Output: list of floats [0-1] representing action probability with added noise
          
docstring: list of floats [0-1] representing action probability distrabution
  Description: add dirichlet noise to probability distrabution
  Output: list of floats [0-1] representing action probability with added noise
docstring_tokens: list of floats [ 0 - 1 ] representing action probability distrabution Description : add dirichlet noise to probability distrabution Output : list of floats [ 0 - 1 ] representing action probability with added noise
code: def dirichlet_noise(self, p):
          """
          Input: p - list of floats [0-1] representing action probability distrabution
          Description: add dirichlet noise to probability distrabution
          Output: list of floats [0-1] representing action probability with added noise
          """
          d = dirichlet([self.d_a] * len(p))
          return torch.tensor(d * self.e_f) + (torch.matmul(torch.tensor([1 - self.e_f]), p))
code_tokens: def dirichlet_noise ( self , p ) : d = dirichlet ( [ self . d_a ] * len ( p ) ) return torch . tensor ( d * self . e_f ) + ( torch . matmul ( torch . tensor ( [ 1 - self . e_f ] ) , p ) )
short_docstring: Input: p - list of floats [0-1] representing action probability distrabution
  Description: add dirichlet noise to probability distrabution
  Output: list of floats [0-1] representing action probability with added noise
short_docstring_tokens: Input : p - list of floats [ 0 - 1 ] representing action probability distrabution Description : add dirichlet noise to probability distrabution Output : list of floats [ 0 - 1 ] representing action probability with added noise
comment: """
          Input: p - list of floats [0-1] representing action probability distrabution
          Description: add dirichlet noise to probability distrabution
          Output: list of floats [0-1] representing action probability with added noise
          """