code: def _get_p_value_for_t_value_from_dist(t_value: Tensor) -> Tensor:
      """Obtain p-value for a given Tensor of t-values. Handle ``nan`` which cannot be passed into torch distributions.
  
      When t-value is ``nan``, a resulted p-value should be alson ``nan``.
  
      """
      device = t_value
      normal_dist = torch.distributions.normal.Normal(
          torch.tensor([0.0]).to(device), torch.tensor([1.0]).to(device)
      )
  
      is_nan = t_value.isnan()
      t_value = t_value.nan_to_num()
      p_value = normal_dist.cdf(t_value)
      return p_value.where(
          ~is_nan,
          torch.tensor(
              float("nan"), dtype=p_value.dtype, device=p_value.device
          ),
      )
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.regression.utils import _check_data_shape_to_num_outputs
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.data import _bincount, _cumsum, dim_zero_cat
  from torchmetrics.utilities.enums import EnumStr
label_desc: Use image node_id 10 for calling a global function with example usage: _get_p_value_for_t_value_from_dist(t_value) and returns: p_value