class_docstr: This is the configuration class to store the configuration of a [`MobileNetV1Model`]. It is used to instantiate a
  MobileNetV1 model according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the MobileNetV1
  [google/mobilenet_v1_1.0_224](https://huggingface.co/google/mobilenet_v1_1.0_224) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      image_size (`int`, *optional*, defaults to 224):
          The size (resolution) of each image.
      depth_multiplier (`float`, *optional*, defaults to 1.0):
          Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32
          channels. This is sometimes also called "alpha" or "width multiplier".
      min_depth (`int`, *optional*, defaults to 8):
          All layers will have at least this many channels.
      hidden_act (`str` or `function`, *optional*, defaults to `"relu6"`):
          The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
      tf_padding (`bool`, *optional*, defaults to `True`):
          Whether to use TensorFlow padding rules on the convolution layers.
      classifier_dropout_prob (`float`, *optional*, defaults to 0.999):
          The dropout ratio for attached classifiers.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 0.001):
          The epsilon used by the layer normalization layers.
  
  Example:
  
  ```python
  >>> from transformers import MobileNetV1Config, MobileNetV1Model
  
  >>> # Initializing a "mobilenet_v1_1.0_224" style configuration
  >>> configuration = MobileNetV1Config()
  
  >>> # Initializing a model from the "mobilenet_v1_1.0_224" style configuration
  >>> model = MobileNetV1Model(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      num_channels=3,
      image_size=224,
      depth_multiplier=1.0,
      min_depth=8,
      hidden_act="relu6",
      tf_padding=True,
      classifier_dropout_prob=0.999,
      initializer_range=0.02,
      layer_norm_eps=0.001,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      if depth_multiplier <= 0:
          raise ValueError(
              "depth_multiplier must be greater than zero."
          )
  
      self.num_channels = num_channels
      self.image_size = image_size
      self.depth_multiplier = depth_multiplier
      self.min_depth = min_depth
      self.hidden_act = hidden_act
      self.tf_padding = tf_padding
      self.classifier_dropout_prob = classifier_dropout_prob
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
  
file_path: transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py
imports: from collections import OrderedDict
  from typing import Mapping
  from packaging import version
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new MobileNetV1Config object from inherited base classes: PretrainedConfig with example: obj = MobileNetV1Config(num_channels, image_size, depth_multiplier, min_depth, hidden_act, tf_padding, classifier_dropout_prob, initializer_range, layer_norm_eps)