class_docstr: Base class for samplers that sample points from a polytope.
  
  :meta private:
class_docstr_tok: ["Base","class","for","samplers","that","sample","points","from","a","polytope",".",":","meta","private",":"]
docstr: Check whether a point is contained in the polytope.
  
  Args:
      x: A `d x 1`-dim Tensor.
  
  Returns:
      True if `x` is contained inside the polytope (incl. its boundary),
      False otherwise.
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code: def feasible(self, x: Tensor) -> bool:
      r"""Check whether a point is contained in the polytope.
  
      Args:
          x: A `d x 1`-dim Tensor.
  
      Returns:
          True if `x` is contained inside the polytope (incl. its boundary),
          False otherwise.
      """
      ineq = (self.A @ x - self.b <= 0).all()
      if self.equality_constraints is not None:
          eq = (self.C @ x - self.d == 0).all()
          return ineq & eq
      return ineq
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from contextlib import contextmanager
  from typing import Any, Generator, Iterable, List, Optional, Tuple, TYPE_CHECKING
  import numpy
  import scipy
  import torch
  from botorch.exceptions.errors import BotorchError
  from botorch.sampling.qmc import NormalQMCEngine
  from botorch.utils.transforms import unnormalize
  from scipy.spatial import Delaunay, HalfspaceIntersection
  from torch import LongTensor, Tensor
  from torch.quasirandom import SobolEngine
label_desc: Use image node_id 2 for calling the PolytopeSampler obj's underlying member method code with example usage: obj.feasible(x) and returns: ineq, unknown