class_docstr: Localization Network of RARE, which predicts C' (K x 2) from I (I_width x I_height) 
class_docstr_tok: ["Localization","Network","of","RARE",",","which","predicts","C","'","(","K","x","2",")","from","I","(","I_width","x","I_height",")"]
docstr: input:     batch_I : Batch Input Image [batch_size x I_channel_num x I_height x I_width]
  output:    batch_C_prime : Predicted coordinates of fiducial points for input batch [batch_size x F x 2]
docstr_tok: ["input",":","batch_I",":","Batch","Input","Image","[","batch_size","x","I_channel_num","x","I_height","x","I_width","]","output",":","batch_C_prime",":","Predicted","coordinates","of","fiducial","points","for","input","batch","[","batch_size","x","F","x","2","]"]
code: def forward(self, batch_I):
      """
      input:     batch_I : Batch Input Image [batch_size x I_channel_num x I_height x I_width]
      output:    batch_C_prime : Predicted coordinates of fiducial points for input batch [batch_size x F x 2]
      """
      batch_size = batch_I.size(0)
      features = self.conv(batch_I).view(batch_size, -1)
      batch_C_prime = self.localization_fc2(
          self.localization_fc1(features)
      ).view(batch_size, self.F, 2)
      return batch_C_prime
  
file_path: EasyOCR/trainer/modules/transformation.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the LocalizationNetwork obj's underlying member method code with example usage: obj.forward(batch_I) and returns: batch_C_prime