docstr: Roughly equivalent to
  
  ```
  for iterator in loader:
      for item in iterator:
          yield infer(item, **params)
  ```
  
          Arguments:
              loader (`torch.utils.data.DataLoader` or any iterator):
                  The iterator that will be used to apply `infer` on.
              infer (any function):
                  The function to apply of each element of `loader`.
              params (`dict`):
                  The parameters passed to `infer` along with every item
docstr_tok: ["Roughly","equivalent","to","``","`","for","iterator","in","loader",":","for","item","in","iterator",":","yield","infer","(","item",",","*","*","params",")","``","`","Arguments",":","loader","(","`","torch.utils.data.DataLoader","`","or","any","iterator",")",":","The","iterator","that","will","be","used","to","apply","`","infer","`","on",".","infer","(","any","function",")",":","The","function","to","apply","of","each","element","of","`","loader","`",".","params","(","`","dict","`",")",":","The","parameters","passed","to","`","infer","`","along","with","every","item"]
code: def __init__(self, loader, infer, params, loader_batch_size=None):
      """
      Roughly equivalent to
  
      ```
      for iterator in loader:
          for item in iterator:
              yield infer(item, **params)
      ```
  
              Arguments:
                  loader (`torch.utils.data.DataLoader` or any iterator):
                      The iterator that will be used to apply `infer` on.
                  infer (any function):
                      The function to apply of each element of `loader`.
                  params (`dict`):
                      The parameters passed to `infer` along with every item
      """
      super().__init__(loader, infer, params)
  
imports: import numpy
  import torch
  from torch.utils.data import Dataset, IterableDataset
label_desc: Use image node_id 1 to create a new PipelineChunkIterator object from inherited base classes: PipelineIterator with example: obj = PipelineChunkIterator(loader, infer, params, loader_batch_size)