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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _image_channel_compress_bottom(inputs, model_hparams, name="bottom"):
 """Compresses channel-wise input pixels into whole pixel representions. Perform conversion of RGB pixel values to a real number in the range -1 to 1. This combines pixel channels to form a representation of shape [img_len, img_len]. Args: inputs: Tensor representing RGB pixel intensities as integers, of shape [batch, img_len, img_len, channels]. model_hparams: HParams, model hyperparmeters. name: string, scope. Returns: body_input: Tensor of shape [batch, img_len, img_len, model_hparams.hidden_size]. """

  num_channels = 3
  with tf.variable_scope(name):
    inputs = tf.to_float(inputs)
    hp = model_hparams
    if hp.mode != tf.estimator.ModeKeys.PREDICT:
      tf.summary.image(
          "inputs",
          common_layers.tpu_safe_image_summary(inputs),
          max_outputs=2)
    inputs = common_layers.convert_rgb_to_symmetric_real(inputs)

    # Reshape inputs to apply convolutions across [img_len, img_len*channels].
    inputs_shape = common_layers.shape_list(inputs)
    inputs = tf.reshape(
        inputs, [-1, inputs_shape[1], inputs_shape[2] * inputs_shape[3], 1])

    # Compress RGB intensities for each pixel using a convolution.
    outputs = tf.layers.conv2d(
        inputs,
        model_hparams.hidden_size,
        kernel_size=(1, num_channels),
        padding="VALID",
        strides=(1, num_channels),
        activation=tf.nn.relu,
        name="conv_input")
    return outputs