parameters: (data, shape=_Null, get_prob=True, **kwargs)
return_statement: return _internal._sample_multinomial(data, shape, get_prob, **kwargs)
docstring: Concurrent sampling from multiple multinomial distributions.
  
      .. note:: The input distribution must be normalized, i.e. `data` must sum to
                1 along its last dimension.
  
      Parameters
      ----------
      data : Symbol
          An *n* dimensional array whose last dimension has length `k`, where
          `k` is the number of possible outcomes of each multinomial distribution.
          For example, data with shape `(m, n, k)` specifies `m*n` multinomial
          distributions each with `k` possible outcomes.
      shape : int or tuple of ints
          The number of samples to draw from each distribution. If shape is empty
          one sample will be drawn from each distribution.
      get_prob : bool
          If true, a second array containing log likelihood of the drawn
          samples will also be returned.
          This is usually used for reinforcement learning, where you can provide
          reward as head gradient w.r.t. this array to estimate gradient.
docstring_summary: Concurrent sampling from multiple multinomial distributions.
docstring_tokens: Concurrent sampling from multiple multinomial distributions .
function: def multinomial(data, shape=_Null, get_prob=True, **kwargs):
      """Concurrent sampling from multiple multinomial distributions.
  
      .. note:: The input distribution must be normalized, i.e. `data` must sum to
                1 along its last dimension.
  
      Parameters
      ----------
      data : Symbol
          An *n* dimensional array whose last dimension has length `k`, where
          `k` is the number of possible outcomes of each multinomial distribution.
          For example, data with shape `(m, n, k)` specifies `m*n` multinomial
          distributions each with `k` possible outcomes.
      shape : int or tuple of ints
          The number of samples to draw from each distribution. If shape is empty
          one sample will be drawn from each distribution.
      get_prob : bool
          If true, a second array containing log likelihood of the drawn
          samples will also be returned.
          This is usually used for reinforcement learning, where you can provide
          reward as head gradient w.r.t. this array to estimate gradient.
      """
      return _internal._sample_multinomial(data, shape, get_prob, **kwargs)
function_tokens: def multinomial ( data , shape = _Null , get_prob = True , * * kwargs ) : return _internal . _sample_multinomial ( data , shape , get_prob , * * kwargs )