class_docstr: A component for computing Document embeddings using OpenAI models.
  The embedding of each Document is stored in the `embedding` field of the Document.
  
  Usage example:
  ```python
  from haystack import Document
  from haystack.components.embedders import AzureOpenAIDocumentEmbedder
  
  doc = Document(content="I love pizza!")
  
  document_embedder = AzureOpenAIDocumentEmbedder()
  
  result = document_embedder.run([doc])
  print(result['documents'][0].embedding)
  
  # [0.017020374536514282, -0.023255806416273117, ...]
  ```
docstr: Embed a list of texts in batches.
code: def _embed_batch(
      self, texts_to_embed: List[str], batch_size: int
  ) -> Tuple[List[List[float]], Dict[str, Any]]:
      """
      Embed a list of texts in batches.
      """
  
      all_embeddings: List[List[float]] = []
      meta: Dict[str, Any] = {
          "model": "",
          "usage": {"prompt_tokens": 0, "total_tokens": 0},
      }
      for i in tqdm(
          range(0, len(texts_to_embed), batch_size),
          desc="Embedding Texts",
      ):
          batch = texts_to_embed[i : i + batch_size]
          response = self._client.embeddings.create(
              model=self.azure_deployment, input=batch
          )
  
          # Append embeddings to the list
          all_embeddings.extend(el.embedding for el in response.data)
  
          # Update the meta information only once if it's empty
          if not meta["model"]:
              meta["model"] = response.model
              meta["usage"] = dict(response.usage)
          else:
              # Update the usage tokens
              meta["usage"][
                  "prompt_tokens"
              ] += response.usage.prompt_tokens
              meta["usage"][
                  "total_tokens"
              ] += response.usage.total_tokens
  
      return all_embeddings, meta
  
file_path: haystack/haystack/components/embedders/azure_document_embedder.py
imports: import os
  from typing import List, Optional, Dict, Any, Tuple
  from openai.lib.azure import AzureADTokenProvider, AzureOpenAI
  from tqdm import tqdm
  from haystack import component, Document, default_to_dict
label_desc: Use image node_id 5 for calling the AzureOpenAIDocumentEmbedder obj's underlying member method code with example usage: obj._embed_batch(texts_to_embed, batch_size) and returns: all_embeddings,  meta