class_docstr: Class which converts the supplied pytorch model into an equivalent model
  which uses abstract operations
docstr: Do the forward pass through the NN with interval arithmetic.
  
  :param x_interval: data in interval form with shape:
  x_interval[batch_size, 2, feature_1, feature_2, ...] where axis=1 corresponds to the [lower, upper] bounds.
  :return: interval predictions
docstr_tok: ["Do","the","forward","pass","through","the","NN","with","interval","arithmetic",".",":","param","x_interval",":","data","in","interval","form","with","shape",":","x_interval","[","batch_size",",","2",",","feature_1",",","feature_2",",","...","]","where","axis=1","corresponds","to","the","[","lower",",","upper","]","bounds",".",":","return",":","interval","predictions"]
code: def abstract_forward(self, x_interval: np.ndarray) -> "torch.Tensor":
      """
      Do the forward pass through the NN with interval arithmetic.
  
      :param x_interval: data in interval form with shape:
      x_interval[batch_size, 2, feature_1, feature_2, ...] where axis=1 corresponds to the [lower, upper] bounds.
      :return: interval predictions
      """
  
      x = torch.from_numpy(x_interval.astype("float32")).to(self.device)
  
      for op_num, op in enumerate(self.ops):
          # as reshapes are not modules we infer when the reshape from convolutional to dense occurs
          if self.reshape_op_num == op_num:
              x = x.reshape((x.shape[0], 2, -1))
          x = op.forward(x)
      return x
  
imports: from typing import List, Optional, Tuple, Union, Callable, Any, TYPE_CHECKING
  import logging
  import warnings
  import numpy
  import torch
  from art.estimators.certification.interval.interval import PyTorchIntervalConv2D, PyTorchIntervalDense, PyTorchIntervalReLU, PyTorchIntervalBounds
  from art.estimators.classification.pytorch import PyTorchClassifier
label_desc: Use image node_id 3 for calling the ConvertedModel obj's underlying member method code with example usage: obj.abstract_forward(x_interval) and returns: x