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.
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code: def forward_fused_softmax(self, input, mask):
      # input.shape = [b, np, sq, sk]
      scale = self.scale if self.scale is not None else 1.0
      return self.fused_softmax_func(input, mask, scale)
  
imports: import torch
  from apex._autocast_utils import _cast_if_autocast_enabled
  from apex.transformer.enums import AttnMaskType
label_desc: Use image node_id 4 for calling the FusedScaleMaskSoftmax obj's underlying member method code with example usage: obj.forward_fused_softmax(input, mask) and returns: self