class_docstr: Linear Projection Video Front-End for Lip Reading
  
  The spatial dimension is flattened and projected to dim_output using a Linear layer.
  This is equivalent to having a convolution layer with a kernel size of the size of the image.
  Circle crop can be used as pre-processing to crop the image as a circle around lips and ignore corner pixels
  
  Args:
      in_channels: number of inputs video channels, 1 for grayscale and 3 for RGB
      in_height: image height
      in_width: image width
      dim_output: output feature dimension for linear projection
      out_channels_first: Whether outputs should have channels_first format (Batch, Dout, Time) or channels_last (Batch, Time, Dout)
      circle_crop: crop the image as a circle before the Linear layer, default to False
      circle_radius: the circle radius, default to 1 for full circle
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docstr: Returns definitions of module input ports.
docstr_tok: ["Returns","definitions","of","module","input","ports","."]
code: def input_types_for_export(self):
      """Returns definitions of module input ports."""
      return OrderedDict(
          {
              "output_signal": NeuralType(
                  ("B", "D", "T"), NeuralType()
              ),
              "length": NeuralType(tuple("B"), LengthsType()),
          }
      )
  
file_path: NeMo/nemo/collections/multimodal/speech_cv/modules/linear_projection_video_front_end.py
imports: from collections import OrderedDict
  import torch
  from torch import nn
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types import LengthsType, NeuralType, VideoSignal
label_desc: Use image node_id 3 for calling the LinearProjectionVideoFrontEnd obj's underlying member method code with example usage: obj.input_types_for_export() and returns: OrderedDict