code: def dropout(
      a: TensorLikeType,
      p: float = 0.5,
      training: bool = True,
      inplace: bool = False,
  ) -> TensorLikeType:
      if inplace:
          raise NotImplementedError
  
      if not training:
          return a
  
      torch._check(
          p <= 1 and p >= 0,
          lambda: f"dropout probability has to be between 0 and 1, but got, {p}",
      )
  
      if p == 1:
          return torch.zeros_like(a)
  
      if p == 0:
          return a
  
      scale = 1 / (1 - p)
      dropout_mask = _dropout_helper(a, 1 - p)
  
      return a * dropout_mask * scale
  
imports: import math
  from functools import wraps
  from typing import Callable, Optional, Union
  import torch
  import torch._prims
  import torch._prims_common
  import torch._refs
  from torch._decomp import register_decomposition
  from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND, NumberType, ShapeType, TensorLike, TensorLikeType
  from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, elementwise_unary_scalar_wrapper, out_wrapper
  from torch._refs import _make_inplace
label_desc: Use image node_id 5 for calling a global function with example usage: dropout(a, p, training, inplace) and returns: unknown, a, torch, a