parameters: (trials, probs, shape=[])
return_statement: return mt.multinomial(trials, probs, shape)
docstring: multinomial(trials, probs) or multinomial(trials, probs, [n, m, ...]) returns
             array of multinomial distributed integer vectors.
  
             trials is the number of trials in each multinomial distribution.
             probs is a one dimensional array. There are len(prob)+1 events.
             prob[i] is the probability of the i-th event, 0<=i<len(prob).
             The probability of event len(prob) is 1.-np.sum(prob).
  
         The first form returns a single 1-D array containing one multinomially
             distributed vector.
  
             The second form returns an array of shape (m, n, ..., len(probs)).
             In this case, output[i,j,...,:] is a 1-D array containing a multinomially
             distributed integer 1-D array.
docstring_summary: multinomial(trials, probs) or multinomial(trials, probs, [n, m, ...]) returns
             array of multinomial distributed integer vectors.
docstring_tokens: multinomial ( trials probs ) or multinomial ( trials probs [ n m ... ] ) returns array of multinomial distributed integer vectors .
function: def multinomial(trials, probs, shape=[]):
      """multinomial(trials, probs) or multinomial(trials, probs, [n, m, ...]) returns
             array of multinomial distributed integer vectors.
  
             trials is the number of trials in each multinomial distribution.
             probs is a one dimensional array. There are len(prob)+1 events.
             prob[i] is the probability of the i-th event, 0<=i<len(prob).
             The probability of event len(prob) is 1.-np.sum(prob).
  
         The first form returns a single 1-D array containing one multinomially
             distributed vector.
  
             The second form returns an array of shape (m, n, ..., len(probs)).
             In this case, output[i,j,...,:] is a 1-D array containing a multinomially
             distributed integer 1-D array."""
      if shape == []:
          shape = None
      return mt.multinomial(trials, probs, shape)
function_tokens: def multinomial ( trials , probs , shape = [ ] ) : if shape == [ ] : shape = None return mt . multinomial ( trials , probs , shape )