<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 <a href="https
Step1:  Computing receptive field parameters of tf.keras.applications models.
Step2:  Bonus stuff
<ASSISTANT_TASK:>
Python Code:
from google.colab import drive
drive.mount('/content/gdrive')
! mkdir gdrive/MyDrive/rf_keras
%cd gdrive/MyDrive/rf_keras
! ls
! git clone https://github.com/google-research/receptive_field.git
! ls
%cd receptive_field/
! ls
! pip install .
! pip install tensorflow
Explanation: <a href="https://colab.research.google.com/github/google-research/receptive_field/blob/master/RF_Keras_Applications.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Copyright 2021 Google LLC.
SPDX-License-Identifier: Apache-2.0
Configuring Google Drive, cloning/installing repo and dependencies.
End of explanation
import tensorflow.compat.v1 as tf
import receptive_field as rf
# Example given here: InceptionV3.
g = tf.Graph()
with g.as_default():
  tf.keras.backend.set_learning_phase(0) # Disable BN learning.
  x = tf.keras.Input([None, None, 3], name='input_image')
  model = tf.keras.applications.InceptionV3(input_tensor=x)
graph_def = g.as_graph_def()
input_node = 'input_image'
output_node = 'conv2d_85/Conv2D'
(receptive_field_x, receptive_field_y, effective_stride_x,
 effective_stride_y, effective_padding_x, effective_padding_y) = (
         rf.compute_receptive_field_from_graph_def(graph_def, input_node,
                                                   output_node))
print(receptive_field_x)
print(receptive_field_y)
print(effective_stride_x)
print(effective_stride_y)
print(effective_padding_x)
print(effective_padding_y)
Explanation: Computing receptive field parameters of tf.keras.applications models.
End of explanation
node_info, name_to_node = rf.get_compute_order(graph_def, input_node_name='input_image')
order_to_info = {}
for _, info in node_info.items():
  order_to_info[info.order] = info
print(len(order_to_info.keys()))
for i in range(len(order_to_info.keys())):
  print(order_to_info[i])
Explanation: Bonus stuff: parsing model graph, printing nodes, etc.
This can be helpful, for example, for finding out the exact node names to use.
End of explanation
<END_TASK>