code: def backward(ctx, grad):
      # grad: [B, C * C]
  
      grad = grad.contiguous()
      inputs, outputs = ctx.saved_tensors
      B, input_dim, degree, output_dim = ctx.dims
  
      grad_inputs = torch.zeros_like(inputs)
      _backend.freq_encode_backward(
          grad, outputs, B, input_dim, degree, output_dim, grad_inputs
      )
  
      return grad_inputs, None, None
  
file_path: NeMo/nemo/collections/multimodal/modules/nerf/utils/torch_ngp/freqencoder.py
imports: import _freqencoder
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.cuda.amp import custom_bwd, custom_fwd
label_desc: Use image node_id 2 for calling the _freq_encoder obj's underlying member method code with example usage: obj.backward(ctx, grad) and returns: grad_inputs,  None,  None