code: def uniform_(tensor, a=0, b=1):
      """Drop-in replacement of `torch.nn.init.uniform_`.
      Note:
          -  if using this function, ensure `a` and `b` do not depend on fan-in,
             fan-out, or other notions of width, e.g. if a = 0, b = 1.
          - `tensor` should have `infshape` attribute set by `set_base_shapes`.
      """
      assert hasattr(
          tensor, "infshape"
      ), "Please call set_base_shapes(...)"
      if a != -b:
          assert (
              tensor.infshape.ninf() == 1
          ), "Sampler for (inf, inf) tensors should have mean 0"
  
      def sampler_(tensor, scale=1):
          _no_grad_uniform_(tensor, a * scale, b * scale)
  
      return constant_std_init_(tensor, sampler_)
  
file_path: NeMo/nemo/collections/nlp/modules/common/megatron/mup/init.py
imports: import math
  import warnings
  import torch
  from torch.nn.init import _calculate_correct_fan, _calculate_fan_in_and_fan_out, _no_grad_fill_, _no_grad_normal_, _no_grad_uniform_, calculate_gain
label_desc: Use image node_id 2 for calling a global function with example usage: uniform_(tensor, a, b) and returns: constant_std_init_