class_docstr: fused operation: scaling + mask + softmax
  
  Arguments:
      input_in_fp16: flag to indicate if input in fp16 data format.
      input_in_bf16: flag to indicate if input in bf16 data format.
      attn_mask_type: attention mask type (pad or causal)
      scaled_masked_softmax_fusion: flag to indicate user want to use softmax fusion
      mask_func: mask function to be applied.
      softmax_in_fp32: if true, softmax in performed at fp32 precision.
      scale: scaling factor used in input tensor scaling.
code: def forward(self, input, mask):
      # [b, np, sq, sk]
      assert input.dim() == 4
  
      if self.is_kernel_available(mask, *input.size()):
          return self.forward_fused_softmax(input, mask)
      else:
          return self.forward_torch_softmax(input, mask)
  
imports: import torch
  import torch.nn
  from megatron.model.enums import AttnMaskType
label_desc: Use image node_id 2 for calling the FusedScaleMaskSoftmax obj's underlying member method code with example usage: obj.forward(input, mask) and returns: self, self