code: def gen_inputs(
      self, input_dim, batch_size, num_frames, right_context_length
  ):
      input = torch.rand(batch_size, num_frames, input_dim).to(
          device=self.device, dtype=self.dtype
      )
      lengths = torch.randint(
          1, num_frames - right_context_length, (batch_size,)
      ).to(device=self.device, dtype=self.dtype)
      return input, lengths
  
file_path: audio/test/torchaudio_unittest/prototype/conv_emformer_test_impl.py
imports: import torch
  from torchaudio.prototype.models.conv_emformer import ConvEmformer
  from torchaudio_unittest.common_utils import TestBaseMixin
  from torchaudio_unittest.models.emformer.emformer_test_impl import EmformerTestMixin
label_desc: Use image node_id 2 for calling the ConvEmformerTestImpl obj's underlying member method code with example usage: obj.gen_inputs(input_dim, batch_size, num_frames, right_context_length) and returns: input,  lengths