Change variable name to prevent shadowing (#21153)

fix: input -> input_string.

docs/source/en/tf_xla.mdx

@@ -83,12 +83,12 @@ check_min_version("4.21.0")
 
 tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="</s>")
 model = TFAutoModelForCausalLM.from_pretrained("gpt2")
-input = ["TensorFlow is"]
+input_string = ["TensorFlow is"]
 
 # One line to create an XLA generation function
 xla_generate = tf.function(model.generate, jit_compile=True)
 
-tokenized_input = tokenizer(input, return_tensors="tf")
+tokenized_input = tokenizer(input_string, return_tensors="tf")
 generated_tokens = xla_generate(**tokenized_input, num_beams=2)
 
 decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
@@ -112,12 +112,12 @@ from transformers import AutoTokenizer, TFAutoModelForCausalLM
 
 tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="</s>")
 model = TFAutoModelForCausalLM.from_pretrained("gpt2")
-input = ["TensorFlow is"]
+input_string = ["TensorFlow is"]
 
 xla_generate = tf.function(model.generate, jit_compile=True)
 
 # Here, we call the tokenizer with padding options.
-tokenized_input = tokenizer(input, pad_to_multiple_of=8, padding=True, return_tensors="tf")
+tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf")
 
 generated_tokens = xla_generate(**tokenized_input, num_beams=2)
 decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
@@ -136,8 +136,8 @@ model = TFAutoModelForCausalLM.from_pretrained("gpt2")
 
 xla_generate = tf.function(model.generate, jit_compile=True)
 
-for input in ["TensorFlow is", "TensorFlow is a", "TFLite is a"]:
-    tokenized_input = tokenizer(input, pad_to_multiple_of=8, padding=True, return_tensors="tf")
+for input_string in ["TensorFlow is", "TensorFlow is a", "TFLite is a"]:
+    tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf")
     start = time.time_ns()
     generated_tokens = xla_generate(**tokenized_input, num_beams=2)
     end = time.time_ns()