%arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg15, %arg16, %arg17, %cst0, %cst1, %arg18, %arg19, %arg20, %arg21) ({}) {cell_clip = 0.000000e+00 : f32, fused_activation_function = "NONE", kernel_type = #tfl<lstm_kernel_type_attr FULL>, proj_clip = 0.000000e+00 : f32} : (tensor<1x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>,...

  mlir::TFL::LSTMKernelType kernel_type;
  switch (value) {
    case tflite::LSTMKernelType_FULL:
      kernel_type = mlir::TFL::LSTMKernelType::FULL;
      break;
    case tflite::LSTMKernelType_BASIC:
      kernel_type = mlir::TFL::LSTMKernelType::BASIC;
      break;
  }
  return mlir::TFL::LSTMKernelTypeAttr::get(builder.getContext(), kernel_type);
}

     cell_clip = 1.000000e+01 : f32, fused_activation_function = "TANH", kernel_type = #tfl<lstm_kernel_type_attr FULL>, proj_clip = 0.01 : f32

  cell_clip: float; // Optional, 0.0 means no clipping
  proj_clip: float; // Optional, 0.0 means no clipping

  // Parameters for LSTM version 2 or above.
  // Basic kernel is only supported in version 2 or above.
  kernel_type: LSTMKernelType = FULL;

  // Parameters for LSTM version 4 or above.
  asymmetric_quantize_inputs: bool;
}

// An implementation of TensorFlow dynamic_rnn with LSTMCell.

  cell_clip: float; // Optional, 0.0 means no clipping
  proj_clip: float; // Optional, 0.0 means no clipping

  // Parameters for LSTM version 2 or above.
  // Basic kernel is only supported in version 2 or above.
  kernel_type: LSTMKernelType = FULL;

  // Parameters for LSTM version 4 or above.
  asymmetric_quantize_inputs: bool;
}

// An implementation of TensorFlow dynamic_rnn with LSTMCell.