code: def squeeze_leading_ones(t):
      # Note: [ Squeezing leading ones ]
      #
      # Squeeze leading ones from t.
      #
      # We want:
      #   (B, j0, ?, ?) + (1, 1, ?, ?) -> (B, j0, ?, ?)
      #   (B, j0, ?, ?) + (1, 1, 1, ?, ?) -> (1, B, j0, ?, ?)  (not yet supported)
      #
      # 1) Squeeze extra ones and grab values from NT
      #   (1, 1, ?, ?) -> (?, ?)   and   (sum(*), ?, ?) -> (B, j0, ?, ?)
      # 2) Do dense broadcasting:
      #   (sum(*), ?, ?) + (?, ?) -> (sum(*), ?, ?)
      # 3) Construct nested tensor
      #   (sum(*), ?, ?) -> (B, j0, ?, ?)
      #
      # If unsqueezing on the 0th dim becomes supported, we would unsqueeze
      # at step (4) and we would need to update this function to record how
      # many ones we unsqueezed.
      while t.shape[0] == 1:
          t = t.squeeze(0)
      return t
  
imports: import functools
  import math
  import operator
  import torch
  from torch.nested._internal.sdpa import jagged_scaled_dot_product_attention
  from .nested_tensor import NestedTensor
  from typing import *
  from torch.fx.operator_schemas import normalize_function
label_desc: Use image node_id 7 for calling a global function with example usage: squeeze_leading_ones(t) and returns: t