code: def kaiming_uniform_(types, args=(), kwargs=None, pg=None):
      r"""
      Fills the Tensors in tensor.local_shards with values according to the method
      described in `Delving deep into rectifiers: Surpassing human-level
      performance on ImageNet classification` - He, K. et al. (2015), using a
      uniform distribution. The resulting tensor will have values sampled from
      :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
      .. math::
          \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
      Also known as He initialization.
      Args:
          tensor: tensor sharded across devices
          a: the negative slope of the rectifier used after this layer (only
              used with ``'leaky_relu'``)
          mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
              preserves the magnitude of the variance of the weights in the
              forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
              backwards pass.
          nonlinearity: the non-linear function (`nn.functional` name),
              recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
      """
      validate_param(kwargs, "kwargs")
      sharded_tensor = kwargs["tensor"]
      validate_param(sharded_tensor, "tensor")
      a = kwargs["a"]
      validate_param(a, "a")
      mode = kwargs["mode"]
      validate_param(mode, "mode")
      nonlinearity = kwargs["nonlinearity"]
      validate_param(nonlinearity, "nonlinearity")
  
      for shard in sharded_tensor.local_shards():
          torch.nn.init.kaiming_uniform_(
              shard.tensor, a=a, mode=mode, nonlinearity=nonlinearity
          )
      return sharded_tensor
  
file_path: pytorch/torch/distributed/_shard/sharded_tensor/_ops/init.py
imports: import torch
  import torch.distributed._shard.sharded_tensor
  from torch.distributed._shard.sharded_tensor import _sharded_op_impl
label_desc: Use image node_id 4 for calling a global function with example usage: kaiming_uniform_(types, args, kwargs, pg) and returns: sharded_tensor