code: def set_floatx(value):
      """Set the default float dtype.
  
      Note: It is not recommended to set this to `"float16"` for training,
      as this will likely cause numeric stability issues.
      Instead, mixed precision, which leverages
      a mix of `float16` and `float32`. It can be configured by calling
      `keras.mixed_precision.set_dtype_policy('mixed_float16')`.
  
      Args:
          value: String; `'float16'`, `'float32'`, or `'float64'`.
  
      Examples:
      >>> keras.config.floatx()
      'float32'
  
      >>> keras.config.set_floatx('float64')
      >>> keras.config.floatx()
      'float64'
  
      >>> # Set it back to float32
      >>> keras.config.set_floatx('float32')
  
      Raises:
          ValueError: In case of invalid value.
      """
      global _FLOATX
      accepted_dtypes = {"float16", "float32", "float64"}
      if value not in accepted_dtypes:
          raise ValueError(
              f"Unknown `floatx` value: {value}. "
              f"Expected one of {accepted_dtypes}"
          )
      _FLOATX = str(value)
  
file_path: keras/keras/backend/config.py
imports: import json
  import os
  from keras.api_export import keras_export
label_desc: Use image node_id 2 for calling a global function with example usage: set_floatx(value) without return types