def relu_layer(x, weights, biases, name=None):
    'Computes Relu(x * weight + biases).\n\n  Args:\n    x: a 2D tensor.  Dimensions typically: batch, in_units\n    weights: a 2D tensor.  Dimensions typically: in_units, out_units\n    biases: a 1D tensor.  Dimensions: out_units\n    name: A name for the operation (optional).  If not specified\n      "nn_relu_layer" is used.\n\n  Returns:\n    A 2-D Tensor computing relu(matmul(x, weights) + biases).\n    Dimensions typically: batch, out_units.\n  '
    with ops.op_scope([x, weights, biases], name, 'relu_layer') as name:
        x = ops.convert_to_tensor(x, name='x')
        weights = ops.convert_to_tensor(weights, name='weights')
        biases = ops.convert_to_tensor(biases, name='biases')
        xw_plus_b = nn_ops.bias_add(math_ops.matmul(x, weights), biases)
        return nn_ops.relu(xw_plus_b, name=name)
Computes Relu(x * weight + biases).

Args:
  x: a 2D tensor.  Dimensions typically: batch, in_units
  weights: a 2D tensor.  Dimensions typically: in_units, out_units
  biases: a 1D tensor.  Dimensions: out_units
  name: A name for the operation (optional).  If not specified
    "nn_relu_layer" is used.

Returns:
  A 2-D Tensor computing relu(matmul(x, weights) + biases).
  Dimensions typically: batch, out_units.
def relu_layer(x, weights, biases, name=None):
    'Computes Relu(x * weight + biases).\n\n  Args:\n    x: a 2D tensor.  Dimensions typically: batch, in_units\n    weights: a 2D tensor.  Dimensions typically: in_units, out_units\n    biases: a 1D tensor.  Dimensions: out_units\n    name: A name for the operation (optional).  If not specified\n      "nn_relu_layer" is used.\n\n  Returns:\n    A 2-D Tensor computing relu(matmul(x, weights) + biases).\n    Dimensions typically: batch, out_units.\n  '
    with ops.op_scope([x, weights, biases], name, 'relu_layer') as name:
        x = ops.convert_to_tensor(x, name='x')
        weights = ops.convert_to_tensor(weights, name='weights')
        biases = ops.convert_to_tensor(biases, name='biases')
        xw_plus_b = nn_ops.bias_add(math_ops.matmul(x, weights), biases)
        return nn_ops.relu(xw_plus_b, name=name)