class_docstr: The patch embedding layer at stage 0.
  
  Args:
      num (`int`): number of repeated sets of layers
class_docstr_tok: ["The","patch","embedding","layer","at","stage","0",".","Args",":","num","(","`","int","`",")",":","number","of","repeated","sets","of","layers"]
code: def __init__(
      self, num, patch_size=7, stride=2, in_chans=3, embed_dim=64
  ):
      super().__init__()
      stem = [
          nn.Conv2d(
              in_chans,
              embed_dim,
              patch_size,
              stride,
              padding=3,
              bias=False,
          ),
          nn.BatchNorm2d(embed_dim),
          nn.ReLU(True),
      ]
      for _ in range(num):
          stem.append(
              nn.Conv2d(
                  embed_dim, embed_dim, 3, 1, padding=1, bias=False
              )
          )
          stem.append(nn.BatchNorm2d(embed_dim))
          stem.append(nn.ReLU(True))
      stem.append(
          nn.Conv2d(embed_dim, embed_dim, kernel_size=2, stride=stride)
      )
      self.conv = nn.Sequential(*stem)
      self.norm = nn.LayerNorm(embed_dim)
      self.apply(init_vit_weights)
  
imports: import torch
  from torch import nn
  from towhee.models.layers.patch_embed2d import PatchEmbed2D
  from towhee.models.layers.mlp import Mlp
  from towhee.models.layers.droppath import DropPath
  from towhee.models.utils.init_vit_weights import init_vit_weights
label_desc: Use image node_id 1 to create a new HeadPatchEmbed object from inherited base classes: nn with example: obj = HeadPatchEmbed(num, patch_size, stride, in_chans, embed_dim)