class_docstr: Apply sequential transformations to input sample.
  Default generate transformed samples of combination number of contained
  transformations.
docstr: Returns samples of combination number of contained transformations..
  
  :param ~textflint.input.component.sample.Sample sample:
      Data sample for augmentation.
  :param int n: Default is 5. Max number of unique augmented output.
  :param str|list: Correspond with transform_objs,
      and indicate which field to apply transformations.
  :param dict kwargs: Other auxiliary params.
  :return: list of Sample
docstr_tok: ["Returns","samples","of","combination","number","of","contained","transformations","..",":","param","~textflint.input.component.sample.Sample","sample",":","Data","sample","for","augmentation",".",":","param","int","n",":","Default","is","5",".","Max","number","of","unique","augmented","output",".",":","param","str|list",":","Correspond","with","transform_objs",",","and","indicate","which","field","to","apply","transformations",".",":","param","dict","kwargs",":","Other","auxiliary","params",".",":","return",":","list","of","Sample"]
code: def _transform(self, sample, n=1, field="x", **kwargs):
      r"""
      Returns samples of combination number of contained transformations..
  
      :param ~textflint.input.component.sample.Sample sample:
          Data sample for augmentation.
      :param int n: Default is 5. Max number of unique augmented output.
      :param str|list: Correspond with transform_objs,
          and indicate which field to apply transformations.
      :param dict kwargs: Other auxiliary params.
      :return: list of Sample
  
      """
      trans_samples = [sample]
      if isinstance(field, str):
          fields = [field] * len(self)
      elif isinstance(field, list):
          assert len(field) == len(self)
          fields = field
      else:
          raise ValueError(
              "Do not support field input {0}".format(field)
          )
  
      for index in range(len(self)):
          cached_samples = []
  
          for trans_sample in trans_samples:
              trans_result = self[index].transform(
                  trans_sample, n=n, field=fields[index], **kwargs
              )
  
              if trans_result:
                  cached_samples.extend(trans_result)
  
          # sample n result from n^2 trans results
          trans_samples = self.sample_num(cached_samples, n)
  
      return trans_samples
  
file_path: textflint/textflint/generation/transformation/pipeline.py
imports: from ..transformation import Transformation
label_desc: Use image node_id 3 for calling the Pipeline obj's underlying member method code with example usage: obj._transform(sample, n, field) and returns: trans_samples