class_docstr: Alexnet implementation.
class_docstr_tok: ["Alexnet","implementation","."]
docstr: Process input.
code: def forward(self, x: Tensor) -> NamedTuple:
      """Process input."""
      h = self.slice1(x)
      h_relu1 = h
      h = self.slice2(h)
      h_relu2 = h
      h = self.slice3(h)
      h_relu3 = h
      h = self.slice4(h)
      h_relu4 = h
      h = self.slice5(h)
      h_relu5 = h
  
      class _AlexnetOutputs(NamedTuple):
          relu1: Tensor
          relu2: Tensor
          relu3: Tensor
          relu4: Tensor
          relu5: Tensor
  
      return _AlexnetOutputs(
          h_relu1, h_relu2, h_relu3, h_relu4, h_relu5
      )
  
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 Alexnet obj's underlying member method code with example usage: obj.forward(x) and returns: _AlexnetOutputs