class_docstr: Parameter to represent a slice of a table batched embedding. The slice will be
  a view of the TBE of shape (num_embeddings, embedding_dim) and contain consistent .grad
  
  unlike nn.Parameter, requires_grad is not present and follows requires_grad of TBE.data
  
  Args:
      data (torch.Tensor): original Data (of a TBE) to make a slice of
      start_offset (int):
      end_offset (int):
      num_embeddings (int):
      embedding_dim (int):
class_docstr_tok: ["Parameter","to","represent","a","slice","of","a","table","batched","embedding",".","The","slice","will","be","a","view","of","the","TBE","of","shape","(","num_embeddings",",","embedding_dim",")","and","contain","consistent",".grad","unlike","nn.Parameter",",","requires_grad","is","not","present","and","follows","requires_grad","of","TBE.data","Args",":","data","(","torch.Tensor",")",":","original","Data","(","of","a","TBE",")","to","make","a","slice","of","start_offset","(","int",")",":","end_offset","(","int",")",":","num_embeddings","(","int",")",":","embedding_dim","(","int",")",":"]
code: def __init__(
      self,
      data: torch.Tensor,
      start_offset: int,
      end_offset: int,
      num_embeddings: int,
      embedding_dim: int,
  ) -> None:
      super().__init__()
      self._original_tensor: torch.Tensor = data
      self._start_offset: int = start_offset
      self._end_offset: int = end_offset
      self._num_embeddings: int = num_embeddings
      self._embedding_dim: int = embedding_dim
      self._init_grad: Optional[torch.Tensor] = None
      if self._original_tensor.requires_grad:
          self.retain_grad()
  
file_path: torchrec/torchrec/distributed/composable/table_batched_embedding_slice.py
imports: from typing import Dict, Optional
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new TableBatchedEmbeddingSlice object from inherited base classes: nn with example: obj = TableBatchedEmbeddingSlice(data, start_offset, end_offset, num_embeddings, embedding_dim)