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: This method overrides the default serializer in order to avoid leaking the `api_key` value passed
  to the constructor.
code: def to_dict(self) -> Dict[str, Any]:
      """
      This method overrides the default serializer in order to avoid leaking the `api_key` value passed
      to the constructor.
      """
      return default_to_dict(
          self,
          azure_endpoint=self.azure_endpoint,
          azure_deployment=self.azure_deployment,
          organization=self.organization,
          api_version=self.api_version,
          prefix=self.prefix,
          suffix=self.suffix,
          batch_size=self.batch_size,
          progress_bar=self.progress_bar,
          meta_fields_to_embed=self.meta_fields_to_embed,
          embedding_separator=self.embedding_separator,
      )
  
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 3 for calling the AzureOpenAIDocumentEmbedder obj's underlying member method code with example usage: obj.to_dict() and returns: default_to_dict