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)
class_docstr_tok: ["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",")"]
docstr: Adjust model's first convolution layer to in_channels, if in_channels not equals 3.
  
  Args:
      in_channels (int): Input data's channel number.
docstr_tok: ["Adjust","model","'s","first","convolution","layer","to","in_channels",",","if","in_channels","not","equals","3",".","Args",":","in_channels","(","int",")",":","Input","data","'s","channel","number","."]
code: def _change_in_channels(self, in_channels):
      """Adjust model's first convolution layer to in_channels, if in_channels not equals 3.
  
      Args:
          in_channels (int): Input data's channel number.
      """
      if in_channels != 3:
          Conv2d = get_same_padding_conv2d(
              image_size=self._global_params.image_size
          )
          out_channels = round_filters(32, self._global_params)
          self._conv_stem = Conv2d(
              in_channels,
              out_channels,
              kernel_size=3,
              stride=2,
              bias=False,
          )
  
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 10 for calling the EfficientNet obj's underlying member method code with example usage: obj._change_in_channels(in_channels) without return types