class_docstr: Transformer Encoder block with self attention mechanism.
  
  Args:
      n_layers (int): The number of sub-encoder-layers in the encoder.
          Defaults to 6.
      n_head (int): The number of heads in the multiheadattention models
          Defaults to 8.
      d_k (int): Total number of features in key. Defaults to 64.
      d_v (int): Total number of features in value. Defaults to 64.
      d_model (int): The number of expected features in the decoder inputs.
          Defaults to 512.
      d_inner (int): The dimension of the feedforward network model.
          Defaults to 256.
      dropout (float): Dropout rate for MHSA and FFN. Defaults to 0.1.
      init_cfg (dict or list[dict], optional): Initialization configs.
docstr: Args:
      feat (Tensor): Backbone output of shape :math:`(N, C, H, W)`.
      data_samples (list[TextRecogDataSample]): Batch of
          TextRecogDataSample, containing valid_ratio information.
          Defaults to None.
  
  
  Returns:
      Tensor: The encoder output tensor. Shape :math:`(N, T, C)`.
docstr_tok: ["Args",":","feat","(","Tensor",")",":","Backbone","output","of","shape",":","math",":","`","(","N",",","C",",","H",",","W",")","`",".","data_samples","(","list","[","TextRecogDataSample","]",")",":","Batch","of","TextRecogDataSample",",","containing","valid_ratio","information",".","Defaults","to","None",".","Returns",":","Tensor",":","The","encoder","output","tensor",".","Shape",":","math",":","`","(","N",",","T",",","C",")","`","."]
code: def forward(
      self,
      feat: torch.Tensor,
      data_samples: Sequence[TextRecogDataSample] = None,
  ) -> torch.Tensor:
      """
      Args:
          feat (Tensor): Backbone output of shape :math:`(N, C, H, W)`.
          data_samples (list[TextRecogDataSample]): Batch of
              TextRecogDataSample, containing valid_ratio information.
              Defaults to None.
  
  
      Returns:
          Tensor: The encoder output tensor. Shape :math:`(N, T, C)`.
      """
      n, c, h, w = feat.size()
  
      feat = feat.view(n, c, h * w).permute(0, 2, 1).contiguous()
  
      valid_ratios = []
      for data_sample in data_samples:
          valid_ratios.append(data_sample.get("valid_ratio"))
      mask = self._get_source_mask(feat, valid_ratios)
  
      output = feat
      for enc_layer in self.layer_stack:
          output = enc_layer(output, mask)
      output = self.layer_norm(output)
  
      return output
  
file_path: mmocr/mmocr/models/textrecog/encoders/nrtr_encoder.py
imports: import math
  from typing import Dict, Optional, Sequence, Union
  import torch
  import torch.nn
  from mmengine.model import ModuleList
  from mmocr.models.common import TFEncoderLayer
  from mmocr.registry import MODELS
  from mmocr.structures import TextRecogDataSample
  from .base import BaseEncoder
label_desc: Use image node_id 3 for calling the NRTREncoder obj's underlying member method code with example usage: obj.forward(feat, data_samples) and returns: output