class_docstr: Deterministic, fixed cost model.
  
  For each (q-batch) element of a candidate set `X`, this module computes a
  fixed cost per objective.
class_docstr_tok: ["Deterministic",",","fixed","cost","model",".","For","each","(","q-batch",")","element","of","a","candidate","set","`","X","`",",","this","module","computes","a","fixed","cost","per","objective","."]
docstr: Args:
      fixed_cost: A `m`-dim tensor containing the fixed cost of evaluating each
          objective.
docstr_tok: ["Args",":","fixed_cost",":","A","`","m","`","-dim","tensor","containing","the","fixed","cost","of","evaluating","each","objective","."]
code: def __init__(
      self,
      fixed_cost: Tensor,
  ) -> None:
      r"""
      Args:
          fixed_cost: A `m`-dim tensor containing the fixed cost of evaluating each
              objective.
      """
      super().__init__()
      self.register_buffer("fixed_cost", fixed_cost)
      self._num_outputs = fixed_cost.shape[-1]
  
file_path: botorch/botorch/models/cost.py
imports: from __future__ import annotations
  from typing import Dict, Optional
  import torch
  from botorch.models.deterministic import DeterministicModel
  from torch import Tensor
label_desc: Use image node_id 1 to create a new FixedCostModel object from inherited base classes: DeterministicModel with example: obj = FixedCostModel(fixed_cost)