code: def check_uniform_continuous_next_after(
      device="cpu", batch_size=32, shape=[1e5], niter=3
  ):
      batch_size = 4
      shape = [100000]
      val_range = [
          np.float32(10.0),
          np.nextafter(np.float32(10.0), np.float32(11.0)),
      ]
  
      pipe = Pipeline(
          batch_size=batch_size, device_id=0, num_threads=3, seed=123456
      )
      with pipe:
          pipe.set_outputs(
              dali.fn.random.uniform(
                  device=device, range=val_range, shape=shape
              )
          )
      pipe.build()
      for it in range(niter):
          outputs = pipe.run()
          data_out = (
              outputs[0].as_cpu()
              if isinstance(outputs[0], TensorListGPU)
              else outputs[0]
          )
          for i in range(batch_size):
              data = np.array(data_out[i])
              assert (
                  val_range[0] == data
              ).all(), f"{data} is outside of requested range"
  
file_path: DALI/dali/test/python/operator_2/test_uniform.py
imports: import nvidia.dali
  from nvidia.dali.pipeline import Pipeline
  from nvidia.dali.backend_impl import TensorListGPU
  import numpy
  import scipy.stats
label_desc: Use image node_id 3 for calling a global function with example usage: check_uniform_continuous_next_after(device, batch_size, shape, niter) without return types