code: def forward(ctx, x, weight, bias, bf16=False):
      if torch.is_autocast_enabled():
          dtype = (
              torch.get_autocast_gpu_dtype()
              if not bf16
              else torch.bfloat16
          )
          x, weight, bias = [
              a.to(dtype=dtype) for a in [x, weight, bias]
          ]
      x = x.contiguous()
      weight = weight.contiguous()
      bias = bias.contiguous()
      ctx.save_for_backward(x, weight)
      batch_shape, n = x.shape[:-1], x.shape[-1]
      batch_dim = np.prod(batch_shape)
      assert (
          batch_dim <= 64 * 1024
      ), "fused_dense only supports dimension at most 64k"
      output = fused_dense_cuda.linear_bias_forward(
          x.reshape(batch_dim, n), weight, bias
      )
      return output.reshape(*batch_shape, output.shape[-1])
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.cuda.amp import custom_bwd, custom_fwd
  import fused_dense_lib
  from src.ops.gelu_activation import gelu_bwd
label_desc: Use image node_id 1 for calling the FusedDenseFuncTD obj's underlying member method code with example usage: obj.forward(ctx, x, weight, bias, bf16) and returns: output