(size, depth, seed=1, device=None)
return result
Generate a standard set of random Z as a (size, z_dimension) tensor.
	With the same random seed, it always returns the same z (e.g.,
	the first one is always the same regardless of the size.)
Generate a standard set of random Z as a (size, z_dimension) tensor.
	With the same random seed, it always returns the same z (e.g.,
	the first one is always the same regardless of the size.)
Generate a standard set of random Z as a ( size z_dimension ) tensor . With the same random seed it always returns the same z ( e . g . the first one is always the same regardless of the size . )
def standard_z_sample(size, depth, seed=1, device=None):
	'''
	Generate a standard set of random Z as a (size, z_dimension) tensor.
	With the same random seed, it always returns the same z (e.g.,
	the first one is always the same regardless of the size.)
	'''
	# Use numpy RandomState since it can be done deterministically
	# without affecting global state
	rng = numpy.random.RandomState(seed)
	result = torch.from_numpy(
			rng.standard_normal(size * depth)
			.reshape(size, depth)).float()
	if device is not None:
		result = result.to(device)
	return result
def standard_z_sample ( size , depth , seed = 1 , device = None ) : # Use numpy RandomState since it can be done deterministically # without affecting global state rng = numpy . random . RandomState ( seed ) result = torch . from_numpy ( rng . standard_normal ( size * depth ) . reshape ( size , depth ) ) . float ( ) if device is not None : result = result . to ( device ) return result