class_docstr: Path to the pre-trained GPT4All model file.
class_docstr_tok: ["Path","to","the","pre-trained","GPT4All","model","file","."]
docstr: Validate that llama-cpp-python library is installed.
docstr_tok: ["Validate","that","llama-cpp-python","library","is","installed","."]
code: def validate_environment(cls, values: Dict) -> Dict:
      """Validate that llama-cpp-python library is installed."""
      if isinstance(values["model_path"], str):
          model_path = values["model_path"]
          model_param_names = [
              "lora_path",
              "lora_base",
              "n_ctx",
              "n_parts",
              "seed",
              "f16_kv",
              "logits_all",
              "vocab_only",
              "use_mlock",
              "n_threads",
              "n_batch",
              "use_mmap",
              "last_n_tokens_size",
          ]
          model_params = {k: values[k] for k in model_param_names}
          # For backwards compatibility, only include if non-null.
          if values["n_gpu_layers"] is not None:
              model_params["n_gpu_layers"] = values["n_gpu_layers"]
  
          try:
              try:
                  if values["n_gpus"] == 0:
                      from llama_cpp import Llama
                  else:
                      from llama_cpp_cuda import Llama
              except Exception as e:
                  print(
                      "Failed to listen to n_gpus: %s" % str(e),
                      flush=True,
                  )
                  try:
                      from llama_cpp import Llama
                  except ImportError:
                      from llama_cpp_cuda import Llama
  
              values["client"] = Llama(model_path, **model_params)
          except ImportError:
              raise ModuleNotFoundError(
                  "Could not import llama-cpp-python library. "
                  "Please install the llama-cpp-python library to "
                  "use this embedding model: pip install llama-cpp-python"
              )
          except Exception as e:
              raise ValueError(
                  f"Could not load Llama model from path: {model_path}. "
                  f"Received error {e}"
              )
      else:
          values["client"] = values["model_path"]
      return values
  
imports: import inspect
  import os
  from typing import Dict, Any, Optional, List, Iterator
  from langchain.callbacks.manager import CallbackManagerForLLMRun
  from langchain.schema.output import GenerationChunk
  from langchain.llms import gpt4all
  from pydantic.v1 import root_validator
  from utils import FakeTokenizer, url_alive, download_simple, clear_torch_cache, n_gpus_global
  from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
  from langchain.llms import LlamaCpp
label_desc: Use image node_id 1 for calling the H2OLlamaCpp obj's underlying member method code with example usage: obj.validate_environment(cls, values) and returns: values