class_docstr: Inception Resnet V1 model with optional loading of pretrained weights.
  
  Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface
  datasets. Pretrained state_dicts are automatically downloaded on model instantiation if
  requested and cached in the torch cache. Subsequent instantiations use the cache rather than
  redownloading.
  
  Keyword Arguments:
      pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'.
          (default: {None})
      classify {bool} -- Whether the model should output classification probabilities or feature
          embeddings. (default: {False})
      num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not
          equal to that used for the pretrained model, the final linear layer will be randomly
          initialized. (default: {None})
      dropout_prob {float} -- Dropout probability. (default: {0.6})
docstr: Calculate embeddings or logits given a batch of input image tensors.
  
  Arguments:
      x {torch.tensor} -- Batch of image tensors representing faces.
  
  Returns:
      torch.tensor -- Batch of embedding vectors or multinomial logits.
docstr_tok: ["Calculate","embeddings","or","logits","given","a","batch","of","input","image","tensors",".","Arguments",":","x","{","torch.tensor","}","--","Batch","of","image","tensors","representing","faces",".","Returns",":","torch.tensor","--","Batch","of","embedding","vectors","or","multinomial","logits","."]
code: def forward(self, x):
      """Calculate embeddings or logits given a batch of input image tensors.
  
      Arguments:
          x {torch.tensor} -- Batch of image tensors representing faces.
  
      Returns:
          torch.tensor -- Batch of embedding vectors or multinomial logits.
      """
      x = self.conv2d_1a(x)
      x = self.conv2d_2a(x)
      x = self.conv2d_2b(x)
      x = self.maxpool_3a(x)
      x = self.conv2d_3b(x)
      x = self.conv2d_4a(x)
      x = self.conv2d_4b(x)
      x = self.repeat_1(x)
      x = self.mixed_6a(x)
      x = self.repeat_2(x)
      x = self.mixed_7a(x)
      x = self.repeat_3(x)
      x = self.block8(x)
      x = self.avgpool_1a(x)
      x = self.dropout(x)
      x = self.last_linear(x.view(x.shape[0], -1))
      x = self.last_bn(x)
      if self.classify:
          x = self.logits(x)
      else:
          x = F.normalize(x, p=2, dim=1)
      return x
  
imports: import os
  import requests
  from requests.adapters import HTTPAdapter
  import torch
  from torch import nn
  from torch.nn import functional
  from .utils.download import download_url_to_file
label_desc: Use image node_id 2 for calling the InceptionResnetV1 obj's underlying member method code with example usage: obj.forward(x) and returns: x