class_docstr: Layers for Location Sensitive Attention
  
  Args:
      attention_dim (int): number of channels in the input tensor.
      attention_n_filters (int, optional): number of filters in convolution. Defaults to 32.
      attention_kernel_size (int, optional): kernel size of convolution filter. Defaults to 31.
docstr: Shapes:
      attention_cat: [B, 2, C]
docstr_tok: ["Shapes",":","attention_cat",":","[","B",",","2",",","C","]"]
code: def forward(self, attention_cat):
      """
      Shapes:
          attention_cat: [B, 2, C]
      """
      processed_attention = self.location_conv1d(attention_cat)
      processed_attention = self.location_dense(
          processed_attention.transpose(1, 2)
      )
      return processed_attention
  
imports: import torch
  from scipy.stats import betabinom
  from torch import nn
  from torch.nn import functional
  from TTS.tts.layers.tacotron.common_layers import Linear
label_desc: Use image node_id 2 for calling the LocationLayer obj's underlying member method code with example usage: obj.forward(attention_cat) and returns: processed_attention