class_docstr: Deterministic, fixed cost model.
  
  For each (q-batch) element of a candidate set `X`, this module computes a
  fixed cost per objective.
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docstr: Evaluate the cost on a candidate set X.
  
  Computes the fixed cost of evaluating each objective for each element
  of the q-batch.
  
  Args:
      X: A `batch_shape x q x d'`-dim tensor of candidate points.
  
  Returns:
      A `batch_shape x q x m`-dim tensor of costs.
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code: def forward(self, X: Tensor) -> Tensor:
      r"""Evaluate the cost on a candidate set X.
  
      Computes the fixed cost of evaluating each objective for each element
      of the q-batch.
  
      Args:
          X: A `batch_shape x q x d'`-dim tensor of candidate points.
  
      Returns:
          A `batch_shape x q x m`-dim tensor of costs.
      """
      view_shape = [1] * (X.ndim - 1) + [self._num_outputs]
      expand_shape = X.shape[:-1] + torch.Size([self._num_outputs])
      return self.fixed_cost.view(view_shape).expand(expand_shape)
  
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 2 for calling the FixedCostModel obj's underlying member method code with example usage: obj.forward(X) and returns: self