parameters: (x, level, noise_shape=None, seed=None)
return_statement: return nn.dropout(x * 1., retain_prob, noise_shape, seed=seed)
docstring: Sets entries in `x` to zero at random, while scaling the entire tensor.
  
    Arguments:
        x: tensor
        level: fraction of the entries in the tensor
            that will be set to 0.
        noise_shape: shape for randomly generated keep/drop flags,
            must be broadcastable to the shape of `x`
        seed: random seed to ensure determinism.
  
    Returns:
        A tensor.
docstring_summary: Sets entries in `x` to zero at random, while scaling the entire tensor.
docstring_tokens: Sets entries in x to zero at random while scaling the entire tensor .
function: def dropout(x, level, noise_shape=None, seed=None):
    """Sets entries in `x` to zero at random, while scaling the entire tensor.
  
    Arguments:
        x: tensor
        level: fraction of the entries in the tensor
            that will be set to 0.
        noise_shape: shape for randomly generated keep/drop flags,
            must be broadcastable to the shape of `x`
        seed: random seed to ensure determinism.
  
    Returns:
        A tensor.
    """
    retain_prob = 1. - level
    if seed is None:
      seed = np.random.randint(10e6)
    # the dummy 1. works around a TF bug
    # (float32_ref vs. float32 incompatibility)
    return nn.dropout(x * 1., retain_prob, noise_shape, seed=seed)
function_tokens: def dropout ( x , level , noise_shape = None , seed = None ) : retain_prob = 1. - level if seed is None : seed = np . random . randint ( 10e6 ) # the dummy 1. works around a TF bug # (float32_ref vs. float32 incompatibility) return nn . dropout ( x * 1. , retain_prob , noise_shape , seed = seed )