class_docstr: RegNet Embedddings (stem) composed of a single aggressive convolution.
class_docstr_tok: ["RegNet","Embedddings","(","stem",")","composed","of","a","single","aggressive","convolution","."]
code: def forward(self, pixel_values):
      num_channels = pixel_values.shape[1]
      if num_channels != self.num_channels:
          raise ValueError(
              "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
          )
      hidden_state = self.embedder(pixel_values)
      return hidden_state
  
imports: from typing import Optional
  import torch
  import torch.utils.checkpoint
  from torch import Tensor, nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_outputs import BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 2 for calling the RegNetEmbeddings obj's underlying member method code with example usage: obj.forward(pixel_values) and returns: hidden_state