class_docstr: SqueezeNet implementation.
class_docstr_tok: ["SqueezeNet","implementation","."]
docstr: Process input.
code: def forward(self, x: Tensor) -> NamedTuple:
      """Process input."""
  
      class _SqueezeOutput(NamedTuple):
          relu1: Tensor
          relu2: Tensor
          relu3: Tensor
          relu4: Tensor
          relu5: Tensor
          relu6: Tensor
          relu7: Tensor
  
      relus = []
      for slice_ in self.slices:
          x = slice_(x)
          relus.append(x)
      return _SqueezeOutput(*relus)
  
imports: import inspect
  import os
  from typing import List, NamedTuple, Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from typing_extensions import Literal
  from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE, _TORCHVISION_GREATER_EQUAL_0_13
label_desc: Use image node_id 2 for calling the SqueezeNet obj's underlying member method code with example usage: obj.forward(x) and returns: _SqueezeOutput