code: def __init__(
      self,
      parent,
      batch_size=13,
      num_channels=3,
      image_size=32,
      depth_multiplier=0.25,
      min_depth=8,
      tf_padding=True,
      last_hidden_size=1024,
      output_stride=32,
      hidden_act="relu6",
      classifier_dropout_prob=0.1,
      initializer_range=0.02,
      is_training=True,
      use_labels=True,
      num_labels=10,
      scope=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.num_channels = num_channels
      self.image_size = image_size
      self.depth_multiplier = depth_multiplier
      self.min_depth = min_depth
      self.tf_padding = tf_padding
      self.last_hidden_size = int(last_hidden_size * depth_multiplier)
      self.output_stride = output_stride
      self.hidden_act = hidden_act
      self.classifier_dropout_prob = classifier_dropout_prob
      self.use_labels = use_labels
      self.is_training = is_training
      self.num_labels = num_labels
      self.initializer_range = initializer_range
      self.scope = scope
  
imports: import unittest
  from transformers import MobileNetV1Config
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 to create a new MobileNetV1ModelTester object  with example: obj = MobileNetV1ModelTester(parent, batch_size, num_channels, image_size, depth_multiplier, min_depth, tf_padding, last_hidden_size, output_stride, hidden_act, classifier_dropout_prob, initializer_range, is_training, use_labels, num_labels, scope)