code: def get_tabular_classifier_hf(load_init=True):
      """
      Standard Huggingface classifier for unit testing on Iris dataset.
  
      :param load_init: Load the initial weights if True.
      :type load_init: `bool`
      :return: Huggingface model for Iris dataset.
      :rtype: :class:`.HuggingFaceClassifierPyTorch`
      """
      import torch
      from transformers.modeling_utils import PreTrainedModel
      from transformers.configuration_utils import PretrainedConfig
      from transformers.modeling_outputs import ImageClassifierOutput
      from art.estimators.classification.hugging_face import (
          HuggingFaceClassifierPyTorch,
      )
  
      class ModelConfig(PretrainedConfig):
          def __init__(
              self,
              **kwargs,
          ):
              super().__init__(**kwargs)
              self.device = torch.device(
                  "cuda:0" if torch.cuda.is_available() else "cpu"
              )
  
      class Model(PreTrainedModel):
          """
          Create Iris model.
  
          The weights and biases are identical to the TensorFlow model in `get_iris_classifier_tf`.
          """
  
          def __init__(self, config):
              super().__init__(config)
  
              self.fully_connected1 = torch.nn.Linear(4, 10)
              self.fully_connected2 = torch.nn.Linear(10, 10)
              self.fully_connected3 = torch.nn.Linear(10, 3)
  
              if load_init:
                  w_dense1 = np.load(
                      os.path.join(
                          os.path.dirname(os.path.dirname(__file__)),
                          "utils/resources/models",
                          "W_DENSE1_IRIS.npy",
                      )
                  )
                  b_dense1 = np.load(
                      os.path.join(
                          os.path.dirname(os.path.dirname(__file__)),
                          "utils/resources/models",
                          "B_DENSE1_IRIS.npy",
                      )
                  )
                  w_dense2 = np.load(
                      os.path.join(
                          os.path.dirname(os.path.dirname(__file__)),
                          "utils/resources/models",
                          "W_DENSE2_IRIS.npy",
                      )
                  )
                  b_dense2 = np.load(
                      os.path.join(
                          os.path.dirname(os.path.dirname(__file__)),
                          "utils/resources/models",
                          "B_DENSE2_IRIS.npy",
                      )
                  )
                  w_dense3 = np.load(
                      os.path.join(
                          os.path.dirname(os.path.dirname(__file__)),
                          "utils/resources/models",
                          "W_DENSE3_IRIS.npy",
                      )
                  )
                  b_dense3 = np.load(
                      os.path.join(
                          os.path.dirname(os.path.dirname(__file__)),
                          "utils/resources/models",
                          "B_DENSE3_IRIS.npy",
                      )
                  )
  
                  self.fully_connected1.weight = torch.nn.Parameter(
                      torch.Tensor(np.transpose(w_dense1))
                  )
                  self.fully_connected1.bias = torch.nn.Parameter(
                      torch.Tensor(b_dense1)
                  )
                  self.fully_connected2.weight = torch.nn.Parameter(
                      torch.Tensor(np.transpose(w_dense2))
                  )
                  self.fully_connected2.bias = torch.nn.Parameter(
                      torch.Tensor(b_dense2)
                  )
                  self.fully_connected3.weight = torch.nn.Parameter(
                      torch.Tensor(np.transpose(w_dense3))
                  )
                  self.fully_connected3.bias = torch.nn.Parameter(
                      torch.Tensor(b_dense3)
                  )
  
          # pylint: disable=W0221
          # disable pylint because of API requirements for function
          def forward(self, x):
              x = self.fully_connected1(x)
              x = self.fully_connected2(x)
              logit_output = self.fully_connected3(x)
              return ImageClassifierOutput(logits=logit_output)
  
      config = ModelConfig()
      pt_model = Model(config=config)
      optimizer = torch.optim.Adam(pt_model.parameters(), lr=0.01)
  
      hf_classifier = HuggingFaceClassifierPyTorch(
          pt_model,
          loss=torch.nn.CrossEntropyLoss(),
          optimizer=optimizer,
          input_shape=(4,),
          nb_classes=3,
          clip_values=(0, 1),
          processor=None,
      )
  
      return hf_classifier
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import json
  import logging
  import os
  import pickle
  import time
  import unittest
  import warnings
  import sklearn
  import numpy
  from art.estimators.classification.tensorflow import TensorFlowV2Classifier
  from art.estimators.encoding.tensorflow import TensorFlowEncoder
  from art.estimators.generation.tensorflow import TensorFlowGenerator, TensorFlowV2Generator
  from art.estimators.gan.tensorflow import TensorFlowV2GAN
  from art.utils import load_dataset
label_desc: Use image node_id 32 for calling a global function with example usage: get_tabular_classifier_hf(load_init) and returns: hf_classifier, ImageClassifierOutput