docstr: This function all_gather all shards and return a tensor that
  is replicated on the previously sharded mesh dimension
docstr_tok: ["This","function","all_gather","all","shards","and","return","a","tensor","that","is","replicated","on","the","previously","sharded","mesh","dimension"]
code: def _to_replicate_tensor(
      self,
      local_tensor: torch.Tensor,
      size: torch.Size,
      mesh: DeviceMesh,
      mesh_dim: int,
  ) -> torch.Tensor:
      """
      This function all_gather all shards and return a tensor that
      is replicated on the previously sharded mesh dimension
      """
      my_coordinate = mesh.get_coordinate()
      num_chunks = mesh.size(mesh_dim=mesh_dim)
  
      if my_coordinate is None:
          # if rank is not part of mesh, we simply return local_tensor,
          # which should be an empty tensor
          return local_tensor
  
      # check if it needs to pad input tensor before all_gather
      full_chunk_size = (size[self.dim] + num_chunks - 1) // num_chunks
      chunk_sizes = [
          max(
              min(size[self.dim], full_chunk_size * (idx + 1))
              - full_chunk_size * idx,
              0,
          )
          for idx in range(num_chunks)
      ]
      pad_sizes = [
          full_chunk_size - chunk_size for chunk_size in chunk_sizes
      ]
      is_padded = size[self.dim] % num_chunks != 0
  
      pad_size = pad_sizes[my_coordinate[mesh_dim]]
      if pad_size > 0:
          local_tensor = self._pad_tensor(local_tensor, pad_size)
      local_tensor = local_tensor.contiguous()
  
      result = funcol.all_gather_tensor(
          local_tensor,
          gather_dim=self.dim,
          group=(mesh, mesh_dim),
      )
  
      # Unpad the tensor if the input tensor was padded
      if is_padded:
          full_pad_size = sum(pad_sizes)
          result = self._unpad_tensor(result, full_pad_size)
      return result
  
imports: from dataclasses import dataclass
  from typing import Any, cast, List, NamedTuple, Optional, Tuple
  import torch
  import torch.distributed._functional_collectives
  import torch.distributed.distributed_c10d
  from torch.distributed._tensor._collective_utils import mesh_broadcast, mesh_scatter
  from torch.distributed.device_mesh import DeviceMesh
label_desc: Use image node_id 7 for calling the Shard obj's underlying member method code with example usage: obj._to_replicate_tensor(local_tensor, size, mesh, mesh_dim) and returns: result, local_tensor