class_docstr: Builds a component pipeline to train and infer models.
docstr: Args:
      x: Names of pipeline inference inputs.
      out: Names of pipeline inference outputs.
      y: Names of additional inputs (targets) for pipeline training and evaluation.
      pipe: List of pipeline elements.
docstr_tok: ["Args",":","x",":","Names","of","pipeline","inference","inputs",".","out",":","Names","of","pipeline","inference","outputs",".","y",":","Names","of","additional","inputs","(","targets",")","for","pipeline","training","and","evaluation",".","pipe",":","List","of","pipeline","elements","."]
code: def __init__(
      self,
      x: Optional[Union[str, list]] = None,
      out: Optional[Union[str, list]] = None,
      y: Optional[Union[str, list]] = None,
      pipe: Optional[List[Element]] = None,
  ) -> None:
      """
      Args:
          x: Names of pipeline inference inputs.
          out: Names of pipeline inference outputs.
          y: Names of additional inputs (targets) for pipeline training and evaluation.
          pipe: List of pipeline elements.
      """
      super().__init__(in_x=x, out_params=out, in_y=y)
      if pipe is not None:
          for element in pipe:
              self.append(
                  element.component,
                  element.x,
                  element.out,
                  element.y,
                  element.main,
              )
  
file_path: DeepPavlov/deeppavlov/core/common/base.py
imports: from types import FunctionType
  from typing import List, Optional, Union
  from deeppavlov.core.common.chainer import Chainer
  from deeppavlov.core.models.component import Component
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: Chainer with example: obj = Model(x, out, y, pipe)