class_docstr: the location-variable convolutions
docstr: perform location-variable convolution operation on the input sequence (x) using the local convolution kernel
  Args:
      x (Tensor): the input sequence (batch, in_channels, in_length).
      kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length)
      bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length)
      dilation (int): the dilation of convolution.
      hop_size (int): the hop_size of the conditioning sequence.
  Returns:
      (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length).
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code: def location_variable_convolution(
      self, x, kernel, bias, dilation=1, hop_size=256
  ):
      """perform location-variable convolution operation on the input sequence (x) using the local convolution kernel
      Args:
          x (Tensor): the input sequence (batch, in_channels, in_length).
          kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length)
          bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length)
          dilation (int): the dilation of convolution.
          hop_size (int): the hop_size of the conditioning sequence.
      Returns:
          (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length).
      """
      batch, _, in_length = x.shape
      batch, _, out_channels, kernel_size, kernel_length = kernel.shape
      assert in_length == (
          kernel_length * hop_size
      ), "length of (x, kernel) is not matched"
  
      padding = dilation * int((kernel_size - 1) / 2)
      x = F.pad(
          x, (padding, padding), "constant", 0
      )  # (batch, in_channels, in_length + 2*padding)
      x = x.unfold(
          2, hop_size + 2 * padding, hop_size
      )  # (batch, in_channels, kernel_length, hop_size + 2*padding)
  
      if hop_size < dilation:
          x = F.pad(x, (0, dilation), "constant", 0)
      x = x.unfold(
          3, dilation, dilation
      )  # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation)
      x = x[:, :, :, :, :hop_size]
      x = x.transpose(
          3, 4
      )  # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation)
      x = x.unfold(
          4, kernel_size, 1
      )  # (batch, in_channels, kernel_length, dilation, _, kernel_size)
  
      o = torch.einsum("bildsk,biokl->bolsd", x, kernel)
      o = o.to(memory_format=torch.channels_last_3d)
      bias = (
          bias.unsqueeze(-1)
          .unsqueeze(-1)
          .to(memory_format=torch.channels_last_3d)
      )
      o = o + bias
      o = o.contiguous().view(batch, out_channels, -1)
  
      return o
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv2d
  from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 3 for calling the LVCBlock obj's underlying member method code with example usage: obj.location_variable_convolution(x, kernel, bias, dilation, hop_size) and returns: o