code: def truncated_normal(
      shape, mean=0.0, stddev=1.0, dtype=None, seed=None
  ):
      # Take a larger standard normal dist, discard values outside 2 * stddev
      # Offset by mean and stddev
      x = normal(
          tuple(shape) + (4,), mean=0, stddev=1, dtype=dtype, seed=seed
      )
      valid = (x > -2) & (x < 2)
      indexes = valid.max(-1, keepdim=True)[1]
      trunc_x = torch.empty(shape, device=get_device())
      trunc_x.data.copy_(x.gather(-1, indexes).squeeze(-1))
      trunc_x.data.mul_(stddev).add_(mean)
      return trunc_x
  
imports: import torch
  import torch._dynamo
  import torch.nn.functional
  from keras.backend.config import floatx
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.core import to_torch_dtype
  from keras.random.seed_generator import SeedGenerator
  from keras.random.seed_generator import draw_seed
  from keras.random.seed_generator import make_default_seed
label_desc: Use image node_id 6 for calling a global function with example usage: truncated_normal(shape, mean, stddev, dtype, seed) and returns: trunc_x