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.
code: def __init__(
      self,
      attention_dim,
      attention_n_filters=32,
      attention_kernel_size=31,
  ):
      super(LocationLayer, self).__init__()
      self.location_conv1d = nn.Conv1d(
          in_channels=2,
          out_channels=attention_n_filters,
          kernel_size=attention_kernel_size,
          stride=1,
          padding=(attention_kernel_size - 1) // 2,
          bias=False,
      )
      self.location_dense = Linear(
          attention_n_filters,
          attention_dim,
          bias=False,
          init_gain="tanh",
      )
  
imports: import torch
  from torch import nn
  from torch.nn import functional
  from TTS.tts.layers.common_layers import Linear
  from scipy.stats import betabinom
label_desc: Use image node_id 1 to create a new LocationLayer object from inherited base classes: nn with example: obj = LocationLayer(attention_dim, attention_n_filters, attention_kernel_size)