code: def _validate_quantile(q):
      if isinstance(q, float):
          q = torch.as_tensor(q)
      if q.ndim == 1 and torch.numel(q) < 10:
          for i in range(torch.numel(q)):
              if not (0.0 <= q[i] <= 1.0):
                  return False
      else:
          if not (torch.all(q >= 0) and torch.all(q <= 1)):
              return False
      return True
  
imports: from typing import Optional, Union, Tuple, Sequence
  import torch
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  import ivy
  from ..statistical import _infer_dtype
  from copy import deepcopy
label_desc: Use image node_id 6 for calling a global function with example usage: _validate_quantile(q) and returns: True, False, False