code: def create_conv_patch_embed(
      *,
      in_channels: int,
      out_channels: int,
      conv_kernel_size: Tuple[int] = (1, 16, 16),
      conv_stride: Tuple[int] = (1, 4, 4),
      conv_padding: Tuple[int] = (1, 7, 7),
      conv_bias: bool = True,
      conv: Callable = nn.Conv3d,
  ) -> nn.Module:
      """
      Creates the transformer basic patch embedding. It performs Convolution, flatten and
      transpose.
  
      ::
  
                                          Conv3d
                                             ↓
                                          flatten
                                             ↓
                                         transpose
  
      Args:
          in_channels (int): input channel size of the convolution.
          out_channels (int): output channel size of the convolution.
          conv_kernel_size (tuple): convolutional kernel size(s).
          conv_stride (tuple): convolutional stride size(s).
          conv_padding (tuple): convolutional padding size(s).
          conv_bias (bool): convolutional bias. If true, adds a learnable bias to the
              output.
          conv (callable): Callable used to build the convolution layer.
  
      Returns:
          (nn.Module): transformer patch embedding layer.
      """
      conv_module = conv(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=conv_kernel_size,
          stride=conv_stride,
          padding=conv_padding,
          bias=conv_bias,
      )
      return PatchEmbed(patch_model=conv_module)
  
file_path: pytorchvideo/pytorchvideo/models/stem.py
imports: from typing import Callable, Tuple
  import torch
  import torch.nn
  from pytorchvideo.layers.convolutions import ConvReduce3D
  from pytorchvideo.layers.utils import set_attributes
label_desc: Use image node_id 3 for calling a global function with example usage: create_conv_patch_embed() and returns: PatchEmbed