class_docstr: EfficientNet model.
     Most easily loaded with the .from_name or .from_pretrained methods.
  
  Args:
      blocks_args (list[namedtuple]): A list of BlockArgs to construct blocks.
      global_params (namedtuple): A set of GlobalParams shared between blocks.
  
  References:
      [1] https://arxiv.org/abs/1905.11946 (EfficientNet)
  
  Example:
      >>> import torch
      >>> from efficientnet.model import EfficientNet
      >>> inputs = torch.rand(1, 3, 224, 224)
      >>> model = EfficientNet.from_pretrained('efficientnet-b0')
      >>> model.eval()
      >>> outputs = model(inputs)
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docstr: Create an efficientnet model according to name.
  
  Args:
      model_name (str): Name for efficientnet.
      in_channels (int): Input data's channel number.
      override_params (other key word params):
          Params to override model's global_params.
          Optional key:
              'width_coefficient', 'depth_coefficient',
              'image_size', 'dropout_rate',
              'num_classes', 'batch_norm_momentum',
              'batch_norm_epsilon', 'drop_connect_rate',
              'depth_divisor', 'min_depth'
  
  Returns:
      An efficientnet model.
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code: def from_name(cls, model_name, in_channels=3, **override_params):
      """Create an efficientnet model according to name.
  
      Args:
          model_name (str): Name for efficientnet.
          in_channels (int): Input data's channel number.
          override_params (other key word params):
              Params to override model's global_params.
              Optional key:
                  'width_coefficient', 'depth_coefficient',
                  'image_size', 'dropout_rate',
                  'num_classes', 'batch_norm_momentum',
                  'batch_norm_epsilon', 'drop_connect_rate',
                  'depth_divisor', 'min_depth'
  
      Returns:
          An efficientnet model.
      """
      cls._check_model_name_is_valid(model_name)
      blocks_args, global_params = get_model_params(
          model_name, override_params
      )
      model = cls(blocks_args, global_params)
      model._change_in_channels(in_channels)
      return model
  
imports: import torch
  from torch import nn
  from torch.nn import functional
  from .utils import round_filters, round_repeats, drop_connect, get_same_padding_conv2d, get_model_params, efficientnet_params, load_pretrained_weights, Swish, MemoryEfficientSwish, calculate_output_image_size
label_desc: Use image node_id 6 for calling the EfficientNet obj's underlying member method code with example usage: obj.from_name(cls, model_name, in_channels) and returns: model