class_docstr: A component for embedding strings using OpenAI models.
  
  Usage example:
  ```python
  from haystack.components.embedders import AzureOpenAITextEmbedder
  
  text_to_embed = "I love pizza!"
  
  text_embedder = AzureOpenAITextEmbedder()
  
  print(text_embedder.run(text_to_embed))
  
  # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
  # 'meta': {'model': 'text-embedding-ada-002-v2',
  #          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}
  ```
docstr: Embed a string using AzureOpenAITextEmbedder.
code: def run(self, text: str):
      """Embed a string using AzureOpenAITextEmbedder."""
      if not isinstance(text, str):
          # Check if input is a list and all elements are instances of Document
          if isinstance(text, list) and all(
              isinstance(elem, Document) for elem in text
          ):
              error_message = "Input must be a string. Use AzureOpenAIDocumentEmbedder for a list of Documents."
          else:
              error_message = "Input must be a string."
          raise TypeError(error_message)
  
      # Preprocess the text by adding prefixes/suffixes
      # finally, replace newlines as recommended by OpenAI docs
      processed_text = f"{self.prefix}{text}{self.suffix}".replace(
          "\n", " "
      )
  
      response = self._client.embeddings.create(
          model=self.azure_deployment, input=processed_text
      )
  
      return {
          "embedding": response.data[0].embedding,
          "meta": {
              "model": response.model,
              "usage": dict(response.usage),
          },
      }
  
file_path: haystack/haystack/components/embedders/azure_text_embedder.py
imports: import os
  from typing import List, Optional, Dict, Any
  from openai.lib.azure import AzureADTokenProvider, AzureOpenAI
  from haystack import component, default_to_dict, Document
label_desc: Use image node_id 4 for calling the AzureOpenAITextEmbedder obj's underlying member method code with example usage: obj.run(text) and returns: dict