docstr: Creates a random tensor for a given device and dtype, and computes the expected clamped
  values given the min_vals and/or max_vals.
  If with_nans is provided, then some values are randomly set to nan.
docstr_tok: ["Creates","a","random","tensor","for","a","given","device","and","dtype",",","and","computes","the","expected","clamped","values","given","the","min_vals","and\/or","max_vals",".","If","with_nans","is","provided",",","then","some","values","are","randomly","set","to","nan","."]
code: def generate_clamp_baseline(
      self, device, dtype, *, min_vals, max_vals, with_nans
  ):
      """
      Creates a random tensor for a given device and dtype, and computes the expected clamped
      values given the min_vals and/or max_vals.
      If with_nans is provided, then some values are randomly set to nan.
      """
      X = (
          torch.rand(100, device=device).mul(50).add(-25)
      )  # uniform in [-25, 25]
      X = X.to(dtype)
      if with_nans:
          mask = torch.randint(
              0, 2, X.shape, dtype=torch.bool, device=device
          )
          X[mask] = nan
  
      if isinstance(min_vals, torch.Tensor):
          min_vals = min_vals.cpu().numpy()
  
      if isinstance(max_vals, torch.Tensor):
          max_vals = max_vals.cpu().numpy()
  
      # Use NumPy implementation as reference
      X_clamped = torch.tensor(
          np.clip(X.cpu().numpy(), a_min=min_vals, a_max=max_vals),
          device=device,
      )
      return X, X_clamped
  
imports: import torch
  import numpy
  from itertools import product, combinations, permutations, chain
  from functools import partial
  import random
  import warnings
  import unittest
  from torch import nan
  from torch.testing import make_tensor
  from torch.testing._internal.common_utils import TestCase, run_tests, skipIfTorchDynamo, torch_to_numpy_dtype_dict, IS_JETSON
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCPU, onlyCUDA, dtypes, onlyNativeDeviceTypes, dtypesIfCUDA, largeTensorTest
  from torch.testing._internal.common_dtype import all_types_and_complex_and, all_types, all_types_and
label_desc: Use image node_id 9 for calling the TestShapeOps obj's underlying member method code with example usage: obj.generate_clamp_baseline(device, dtype) and returns: X,  X_clamped